“Our capacity to go beyond the machine rests in our power to assimilate the machine. Until we have absorbed the lessons of objectivity, impersonality, neutrality, the lessons of the mechanical realm, we cannot go further in our development toward the more richly organic, the more profoundly human.”
Lewis Mumford Technics and Civilization, 1934Analyzing 3D Architectural Form Using Deep Learning: A Case Study of Wooden Churches from the Carpathian Mountains
Michael Hasey B.Arch, M.Arch, M.S.CD
Thesis Research
Carnegie Mellon University
Master of Science in Computational Design
This thesis would not have been possible if not for the continued support from my colleagues. To begin, I would like to thank my primary thesis advisor Daniel Cardoso Llach for encouraging me to pursue and combine my passion for both cultural heritage and deep learning within this thesis. His enthusiasm and energy helped me maintain the momentum I needed to successfully complete this work. Next, I would like to thank my secondary advisor Jinmo Rhee for the time he spent expanding my knowledge and understanding of neural networks and deep learning as applied to architectural form. With his assistance and guidance, the most technically challenging aspects of this work could be carried out. I’d also like to thank Jason Zhang for his help constructing my church dataset using his NeRS 3D reconstruction technique. Without his technical assistance, hardware support, and great interest in my work, building this challenging dataset would have been impossible. I would also like to thank Mykhailo Syrokhman for sharing so many wonderful photographs of both ex isting and lost Carpathian wooden churches from his personal archives. In addition to providing essential photo material needed to build my dataset, he also shared a great deal of knowledge about these churches that would have been difficult to discover on my own. I look forward to many more conversations over the phone between Pittsburgh and Uzhorod, Ukraine and, I hope, an opportunity to see these wonderful churches in person one day. Finally, I would like to thank my parents and grandparents for ensuring that Ukrainian culture remains an important aspect of my life and diaspora identity, regardless of the length of time that has passed since our family left Ukraine in the 1890’s. Finally, I would like to thank my wife Erica for her encouragement to keep going, take much needed breaks, and persevere throughout this entire process.
Recent research into 3-D architectural and urban form analysis using deep learning (DL) methods has shown potential to extract and identify intrinsic features from large collections of building data and draw novel insights into how our built environment is configured and interrelated. In order to explore these new ca pabilities, this thesis offers a detailed case study of a critical engagement with building data and DL techniques for the purposes of architectural-historical form analysis. For a case study, it documents the creation of a custom dataset of 3-D meshes of 313 wooden churches located within the Carpathian Mountain regions centering around Ukraine using photographic data and 3-D reconstruction techniques. Though the subject of ongoing scholarly interest, the numerously complex regional style variations of these churches has made it difficult to establish agreed upon formal rules that govern both their stylistic differences and similarities. As a result, this lack of consensus has led to conflicting findings and ongoing contention amongst researchers. Given this challenge, this thesis attempts to demonstrate how recent statistic-based rather than traditional heuristic approaches might provide an alternate way to obtain new insight into this issue. For example, by offering a way to search for and identify both complex and nuanced form relationships and patterns among hundreds of churches of various styles at once within a single diagram. This includes detecting where sty listic overlap occurs, identifying groups of hybrid-styled churches that incroporate multiple styles at once, uncovering unique, yet difficult to detect endemic micro styles, and revealing broad form patterns that illustrate how complex architectural styles are interrelated and incrementally morph from one to the other within both latent and geographic space. The thesis thus offers a path for DL-based form analysis techniques to be put in conversation with traditional architectural studies, helping identify strengths and weaknesses, as well as opportunities for hybrid and cross-methodological architectural-historical analyses. Indirectly, this thesis also demonstrates an alternate method of architectural documentation through a combination of 3D building reconstruction from sparse imagery and architectural style encoding using DL-methods. Consider ing the threat to Ukrainian culture given the current invasion by and war with Russia, digitally preserving both the individual churches themselves through 3D reconstruction and the rules that define their styles through DL-methods, has the potential to document and protect these essential and irreplaceable objects of Ukrainian architectural folk heritage.
Thesis Supervisor: Daniel Cardoso Llach
Title: Assistant Professor
1 2 3 4
Introduction
Contributions Data Methods
Findings
Conclusion Limitations & Future Work
References People Appendices
1.1 1.2 1.3 1.4 1.5 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 6.1 6.2 6.3 10.1 10.2 10.3
Introduction Background Related Work & Next Steps Building Typology Existing Church Research
Overview & Project Timeline Dataset Construction Model Training Model Analysis Techniques Model Analysis - Primary Clusters Model Analysis - Primary Clusters vs. G.T. Labels Model Analysis - Secondary Clusters Model Analysis - Tertiary Clusterts Conclusion Findings Summary Discussion -----------
Appendix A: Latent Space Reference Appendix B: Map Space Reference Appendix C: Dataset Index
5 9 25 35 77 89 93 101 105 147 155 165 183 195 209 215 239 243 247 243 255 261 268 269 270
Architectural form analysis helps architects and researchers identify spatial rules such as proportion, composition, symmetry, and repetition, allowing them to understand the cultural significance of architecture and its relationship to a wider ecosystem of built form and human creativity. However, architectural form is often difficult to describe due to its complex three-di mensional state and physical features. To address this, various techniques have been attempted to quantitatively describe and compare architectural forms; through comparative architectural drawings and diagramming [1], mathematical rules and style guides [2, 3], shape grammars [4, 5], and parametric rule-setting [7]. Generally, these techniques are suitable for closely examining the morphological traits of a single or small collection of architectural works. However, to examine large collections of hundreds or thousands of buildings at a time, a data-scientific approach is used to reveal complex morphologi cal patterns from digital architectural representations. For this approach, architectural and urban form analyses have recently interwoven with inductive methods such as machine learning (ML) and deep learning (DL). They have shown potential to extract and identify intrinsic features from large collections of form data and draw novel insights into how our built envi ronment is configured [1,2,3,4,5]. Since DL methods rely on large datasets formulated for research purposes, it becomes important to define avenues for critically engaging with the production of architectural and urban datasets.
This paper thus offers a case study of a critical engagement with a custom three-dimensional (3D) architectural dataset construction and DL-based analysis for investigating form patterns from large collections of wooden churches locat ed within and around the Central Carpathian Mountain region of Western Ukraine. Since the mid 19th century, Carpathian wooden churches have been the subject of ongoing scholarly interest, delving into their forms, histories, and distribution [8, 12,16, 19, 20]. Though rigorously documented, their complex regional style variations across broad territories have made it difficult to establish comprehensive rules that define their differences and similarities, leading to ongoing contention amongst researchers [10]. Using DL models, the analysis in this paper demonstrates the potential of combining data-scientific approaches with heuristic techniques to reveal new insights into the study of church form. For example, we found that tower height and placement were the most significant architectural indicators of a primary church styles within this region. By observing this pattern, we were able to then observe linear relationships between increasing tower heights and the transition from one style to another. We were also able to observe how church forms transition from one style to another over geograph ic and latent space in an incremental, rather than abrupt manner. Furthermore, we demonstrate how certain churches, though similar in appearance, may be part of two different architectural style groups. We then show how this often occurs within the blurred border zones between two cultural areas. Finally, we demonstrate how difficult to detect church micro-styles, of which only a handful might exist within geographically secluded areas, can be discovered and located using our multi-modal technique.
With these hybrid and cross-methodological approaches, the paper offers paths for new form analysis techniques to be put in conversation with traditional architectural studies. These techniques are expected to open up different perspectives on the study of historic architecture and style variations which are often the site of contestation between different groups, cultures, and regions. Furthermore, as the author is of Ukrainian heritage whose descendants are from Bukovina, a unique historic region with its own distinct style of Carpathian wooden church, this thesis is furthermore a means to learn about and bring awareness to the culturally significant folk architecture of his ancestral roots.
Architectural form analysis is often thought of as being a fairly recent practice, but has in fact, a long and rich history that has been part of a larger and slowly evolving effort to discretize building morphometry using mathematics and rule-based systems. Within Classical architecture, one of the earliest records to an alyze building form through mathematic encoding was conducted by Marcus Vitruvius Pollio, whom in his 100 BCE publication De Architectura attempted to understand the rules that governed architectural form by abstracting and reducing the complexity of architectural representation [8,9].
Figure 3: Plate from De Architectura, 100 BCE.
Figure 4: Plate from the Bible Moralisee, 1230 CE.
1230 CE
These ideas continued to be re-examined within the 1230 CE publication CE Bible Moralisée which, through a religious lense, expresses the vital bond between all physical forms on earth and mathematics. A particularily interesting illustration influenced by Ancient Greek geometry and icons of the Eastern & Orthodox Church, shows “god as an architect and geometer” thus expressing a relation between our world and divine vision which integrates the rules of mathematics.
In 1458, Leon Battista Alberti published one of the earliest books on architecture called De Re Aedificatoria, which pro vided a general survey of desireable buildings including their floorplans, elevations, perspective views and building detail drawings. Here, he created a scientific-like guide which began to define the mathematical rules that defined various building types. Once understood, these rules could be used as prescriptions for similar designs [2].
Later on in 1508, Andrea Palladio published I Quattro Libri Dell’Architettura, a foundational book within the field of archi tecture which “set out rules of classical architectural usage in much the same way as a traditional grammar sets out rules of Latin usage” [Stiny & Mitchell 1978]. Split into four seperate publications; building materials and techniques, private homes & country villas, city planning, and building examples, Palladio explicitely defined systematic rule-sets for both de sign and construction. In this manner, seemingly complex building designs and complicated construction techniques could be simplified and abstracted into their essential components and procedures. With walls, for example, Palladio defines 5 rule-sets for thier construction based on wall thickness.
In his book “Complete Works on Architecture & Perspective” 1537, Se bastiano Serlio created a manual for architects, in which he extract ed and systematized proportional relations from classical buildings into a grammar and syntax. In it, he included an illustration of 50 doorway portals which he proposed are the results of the combina tion of the five orders’ elements . This is one of the earliest studies that uncovered design patterns through the study of numerous ex amples of similar forms. It also highlights the importance of using geometry as a compositional tool to understand repeating form.
In 1800, Jean-Nicholas Louis Durand introduced the idea of generation from a kit of parts as a system of form-making. He suggests that new building designs can be derived through the systematic combination of its essential components; such as walls, doors, columns, roofs. Furthermore, he believed that openings are considered elements as well, as they are com bined to make architecture spaces, which create buildings, and buildings compiled are cities. His overall theory was inspired by the study of natural sciences, such as Hauys rules for crys tal formations & botany which also influenced his idea of “classifying” buildings.
“The idea was to form theoretical principles, which may be applied in designing new buildings, of new forms, to answer new programs and new circumstances.”
- Steadman (1979)
Figure 7: Images of portals as part of Serlio’s studies on geometric composition, symmetry, and beauty.(left)
Figure 8: Diagrams from “Recueil et Prallele des Edifeces de Tout Genre” (1800) showing various building components (above bottom) and how when recombined, can create various building configurations (above top)
As theories of evolution began to take hold from the mid-19th century onward, research was driven to ex plore and analyze the deeper underlying and interconnected rules that drove the form of not only particular building typologies (hospitals, churches, schools, etc.), but relationships between typologies. Though not architecture related, early theories by D’Arcy Wentworth Thompson — on Growth and Form [10], Noam Chomsky — Syntactic Structures [11], and Fredrick Brooks—An Experiment in Musical Composition [12], began to define these deeper foundational rules as not only a means to break down form complexity into abstractions but as a means to create something new when these very abstractions are recombined in new ways. For example, the rules that define the form of certain species of fish according to Thompson [10], the sentence structure of the English language as described by Chomsky [11], or the musical composition of piano music proposed by Brooks [12].
In his 1917 publication, “On Growth and Form”, D’Arcy Wentworth Pioneered the use of mathe matics in biology, and helped to defeat mystical ideas of vitalism, which is the belief that living things are different than non-living things because they are guided by mysterious rules that differ than those guiding non-living entities. However, he argues that natures morphology (form) needs to be described by mathemat ics. He belives these underlying rules explain both the similar and variable morphology found within the same species. Many see him as a pioneer of parametric design, where he broke down complex forms into simple rules & ideas that can be recombined to create something new.
Figure 9: Images of fish “On Growth and Form”, 1917 and how rules of proportion, when warped, help explain their variety of forms. (below)
Figure 10: Plates from “Aesethic Measure” (1933) illustrating creative works that can be explained using his beauty formula (opposite)
In his 1933 Publication “Aethetic Measure”, George David Birkhoff attempt ed to mathematicaly encode and capture the structure and “beauty” of var ious creative works from music to paintings. While conducting his studies, he realized that pattern play an important role in aesethetic perception of many of these art forms. The mathematical core of his theory was the for mula M=O/C where “Aesthetic Measure” or beauty (M) can be described by dividing “Aesethic Order” (O) by complexity (c). With this in mind, he believes that as beauty increases, complexity decreases and order increases. He then goes on to apply this analytical method to paintings, architecture, vases, musical melodies, and poetry as a means to better understand their beauty or lack thereof.
In his 1956 publication, “Syntactic Structures”, Noam Chomsky presents a “Transformational-generative grammar [which] is a broad theory used to model, encode, and deduce a native speaker’s linguistic capabilities” [67]. “Chomsky’s theory posits that language consists of both deep structures and surface structures: Outward-facing surface structures relate pho netic rules into sound, while inward-facing deep structures relate words and conceptual meaning.” (Baughman et al. 2006.)
Brooks 1957 paper, “An Experiment in Musical Composition” was “an early attempt to program a computer to perform “inductive” reasoning on its own, ie, determine the rules that guide some sort of phenomenon, such as a piece of written music in this case.” [68] Here, he attempted to have a computer “inductively analyze a sample of compositions, and, using its conclusions, deductively synthesize new but original compositions” [68]. These induc tions can be performed by determining the probabilities of note sequences. “The simplest way to perform such tasks is for a human to analyze some sample of the type of structure desired, draw up some explicit rules and con straints, and allow the machine to operate deductively” [69]
Figure 12: Diagram illustrating the general method of synthesizing music through analysis.
Within architecture, ideas of describing creative works using mathematics were also explored by G. Stiny and W. Mitchell within their development of a parametric Shape Grammar that defined the general rules of ground plans of Palladio’s villas as a definition of the Palladian style [62] (Fig-ure 1-2). This was based on G. Stiny and J. Gipps 1971 Shape Grammars paper which aims to produce a set of rules, or “grammars” that explain the patterning or form of design objects, and then generate new designs based on these rules.
Figure 13: Examples of villa floorplans created by the Palladian Style grammar (above).
Figure 14: A compositional outcome created by following a simple set of shape grammar rules from “Shape Grammars”, 1971. (below)
In his 1986 thesis dissertation “More than the sum of parts: the grammar of Queen Anne houses” developed at Carnegie Mellon University, U. Flemming introduced a shape grammar that described and generated the form of houses in the Queen Anne Style typical to Pittsburgh’s historic Shadyside district. Here, he devel oped two different Grammars; one for house floor plans, and a second which articulates the plans into 3D forms. Both grammars explain how individual building parts and architectural features are related to each other and can be combined to create infinite variations of houses in this style. This is one of the earliest examples that demonstrates how a shape grammar approach can be used to analyze and generate the archi tectural style of a particular building type in three dimensions.
15: Sequential order of creating and applying a shape grammar to create house in Queen Anne Style.
In his 2001 publication “Customizing Mass Housing: A Discursive Grammar for Siza’s Malagueira Houses”, Jose Pinto Duarte created a grammar that analyzes and generates houses in the style of Albaro Siza house’s at Malagueira. He proposes a process of providing mass-customized housing based on computer design systems. It focuses on the design part, which consists of an interactive system to generate design solutions based on a mathematical model called discursive grammar (shape grammar, a description grammar, & set of heuristics which quantitatively describe the particular styles of these buildings which have to be created manually). Siza himself couldn’t distinguish generated designs via this system from his own However, it cant generate all possible solutions and the overall grammar takes a very long time as you need to acquire domain knowledge and develop a pure grammar.
In his 2007 publication “Style Grammars for Interactive Visualization of Architecture”, Daniel G. Aliaga “Provides a way to quickly visualize existing or novel building structures” [5] by using a repitoire of buidling grammars to facilitate the visualization and modification of architectural structures. This is done by users who draw a simple building block, and the system completes the building in the style of another building. This is done by creating grammars of building styles based on user subdivided building models (floors, windows, doors, trim, brick, wood, etc) and captured photographs (mapped to 3D model). Simply, it maps photos to 3D model, then asks user to sub-divide building into its parts, then it generates a grammar based on the subdivision and applies that to a new geometry.
Compared to Deep Learning methods, the computation aspect is deductive based on user-set subdivisions. thus missing out on greater resolution, relationships, and patterns that more deeply de scribe building form.This work foreshadows the future development of style-transfer algorithms that achieve similar results, though using AI to define and apply architectural style to target forms.
Figure 17: Building facades (shown as different colors) automatically applied to a user defined building volume by a semi self-updating facade composition grammar.
An early and ambitious proposal was demonstrated by V. Moosavi in his 2017 paper “Urban Morphology Meets Deep Learning” [20] where he used DL methods to identify, encode and organize the urban forms of one million cities around the world into distinct form categories. By representing urban forms as two-dimensional 256×256-pixel images of city road networks and encoding them as a 640-dimensional urban latent vector repre-sented as 640 individual float point values using a deep-convolutional autoencoder, Moosavi was able to cluster them into 2000 groups. Then, he generated a type of “urban spectrum” that visually showcased the diversity of these clusters in an easy-to-understand diagram which organized urban form accordingly. By sampling from this spectrum, specific urban forms can be viewed as well as others that share similar organizations, with degree of similarity informed by the Euclidean distance between urban vectors. He then assigned a unique color to each cluster and its corresponding urban forms and mapped them back to geographic space, thus showing the geographic dispersion of these various urban form types around the world.
Another related project includes the 2021 study of urban blocks of Chinese cities by Chenyi Cai et al. [21]. In this study, Cai constructed and analyzed the form patterns within a custom dataset of 3817 urban residential plots or blocks in an attempt to classify them according to nuanced form similarities. Here, blocks were represened as two-dimensional images, where each image contained a gray shape that represents the area or footprint of the urban block and smaller pink shapes that represent the footprints of buildings within the block. Each block within his dataset was then encoded as a 2048 di mensional latent vector using GoogLeNet, a deep convolutional neural network built specifically for image classification via feature mapping. Afterwards, each of the 3817 latent vector representations of city blocks were clustered according to similar spatial layouts. Similar to Moosavi, a diagram was created that showed all blocks organized according to these clusters and their relation to one another. Individual blocks that share “similar” form, which is determined according to Euclidean distance between each urban vector, can be visualized. In this case, urban vectors that are closer together suggest increased form similarity, while urban vectors further apart suggest decreased form similarity.
When considering 3D architectural form, Jinmo Rhee and Pedro Veloso proposed a method to capture the 3D morphological features of 4,956 high-rise buildings sourced from 31 different cit ies around the world [22]. Each building was represented by a series of 256×256-pixel images representing horizontal slices of each high-rise taken at various height increments. By using an IntroVAE model [19], they were able to encode each image as a 10-dimensional latent vector representation and cluster by similar form. By doing this, they identified 14 different types of high-rise buildings by the different configuration of building components such as podiums and towers. They then proposed a typical form for each group, as shown in figure 25, to visualize and qualitatively evaluate the types. The typical forms were determined by taking the nearest building data point to the cluster’s centroid. Based on the typo-morphological investigation, they introduced an intriguing method of generating new forms of high-rise building by adjusting the latent space distribution learned by the model with impressive results. It must also be stated that this paper greatly inspired this research as it was one of the earliest and effective papers to describe the potential of DL-methods in the analysis and generation of 3D form.
Figure 25: Primary tower types within dataset.
Figure 26: Custom tower design interface using DL-based generative process.
Figure 27: Interpolation between tower forms
Figure 28: Design interprations based on synthesized building forms.
Both Moosavi, Cai, and Rhee & Veloso demonstrate powerful new methods for encoding and clustering large volumes of urban and architectural morphometry ac cording to similar forms. This allows them to quickly parse through and illustrate the broad range of styles, configurations and relationships that exist within our built environments. However, a finer grain understanding of these patterns and relationships might be obtained if considering the various forces that have helped shape an objects appearance. For instance, particular climactic conditions or histor ic building traditions. This potential is noted by Rhee and Veloso who believe that “by integrating geometric and other social, economic, cultural, and environmental data, form can be more broadly interpreted as a phenomenon of human activities.” [22]. By hinting that there is more to learn, Rhee and Veloso open the door to new types of DL-based form analysis methods that take into consideration the broader range of elements and conditions that drive architectural morphometry. As a re sponse, my thesis intends to continue this line of inquiry and investigate how this novel approach might help us better understand the vast diversity of architectural forms in our world and their deeper connection to both people and nature.
Figure 29: Women climbing stairs towards the church of St. Nicholas in teh town of Budești Josani, Romania. From www.relevee.uauim.ro/m514/
Describing the Wooden Churches of the Central Carpathian Mountain Region
The wooden churches of the central Carpathian Mountain region can be generally described as unique examples of a broader, yet long-lost wooden architectural heritage. Unlike their urban counterparts, the rural and historically remote Carpathian wooden churches were subjected to far fewer hazards. For example, risk from fire, vandalism, arson, and replacement with stone architecture [24] which was much more common in more populated urban areas. As such, Carpathian wooden churches pro vide a unique glimpse into a wooden architectural heritage that has generally disappeared within the rest of Eastern, Central, and Western Europe [24].
Figure 30: Church of the Translation of the Relics of Saint Nicholas. A Boyko church in the town of Volosianka, Ukraine. Image sourced from Wikipedia.
Geographically, the majority of these churches are located within or nearby the Carpathian Mountains, an important range that has historically been a major divider between Eastern and Western cultures. Rising from the ground from its most Southeastern zone in Eastern Serbia and reaching a maximum elevation of 8,711 feet, the Carpathian Mountain range runs in a linear fashion first Eastwards into Romania, then Northward into Western Ukraine, and then suddenly back Westward while straddling the border between Northern Slovakia and Southern Poland. This study will focus on the central Car pathian region, within, and surrounding, Western Ukraine, and as a result, will include the primary cultural groups of the area, including the Lemko, Boyko, Hutsul and Transcarpathian cultures.
https://localhis tory.org.ua/texts/interviu/bliashanii-pantsir-tse-kinets-tserkvi-doslidnik-derevianikh-tserkov-mikhailo-sirokhman/
For centuries, Byzantine and Ottoman culture from the East, and Baroque and Gothic culture from the West simultaneously intermingled with local mountain communities, providing fertile grounds for cultural synthesis [9, 10]. This condition gave rise to unique ethnic groups, each producing their own particular religious beliefs, clothing, music, food, and philosophies [12].
As a result, the wooden architecture of this region reflects a rich blend of both Eastern and Western motifs [9], for instance, as embodied within the distinct stepped roofs of Boyko churches (Figure 33), which are thought to have originated from the Caucasus, or possibly even earlier from the ancient Arian Zoroaster temples of present day Iran [9].
Poland
Slovakia
Slovakia Romania
Beyond their representation of a lost wooden architectural heritage, and more impor tantly, these churches reflect the distinct folk architecture cultures of the various ethnic groups of the region, with each having their own easily recognizable building style defined by their particular traditions, cultural, spiritual, and climactic needs [34, 35]. This study will focus on the central Carpathian region, within, and surrounding, Western Ukraine, and as a result, will include the primary cultural groups of the area, including the Lemko, Boyko, Hutsul and Transcarpathian (including Marmures) cultures.
Figure 35: Church of St. Nicholas, 1751, in the village of Rekity, Mizhiria district, Ukraine. Image from Mykhailo Syrokhman’s personal archive. (left)
Figure 36: Maps howing the various architectural cultural regions based on Y. Taras’s recent research. Diagram by author. (above)
Figure 37: The Transcarpathian styled Church of St. Nicholas the Wonderworker (1470) in the village of Colodne, Ukraine. Image from www.zaktour.gov.ua/historical-objects
Figure 38: The Lemko styled Church of St. Michael the Archangel (1927) in the village of Conora, Ukraine. Currently located in the Museum of Folk Architecture in Kiev, Ukraine. Image from www.about-ukraine.com/ pokrovska-tserkva-z-s-kanora-ploske-1792-r-kiiv/
To better understand the complex landscape of wooden church morphology from this region, we can generally partition it into four primary styles, or what is commonly referred to as “Schools” of folk temple construction [24]. This include the Boyko, Lemko, Hutsul, and Transcarpathian schools, which are the result of the design inclinations of four distinct cultural groups. In terms of construction date, the vast majority still standing were built between the 18th and 19th centuries,
Figure 39:
Figure 40:
though some are as old as the 17th and even 16th century. Other schools of wooden folk temple construction are also in cluded in the dataset such as Bukovinia, Pokut, Podilia, Opilska, Przemsyl, Volhynia, Bessarabia. and Transylvanian school though lesser in number. However, the paper will only focus on the main four primary schools represented in our dataset, being the Boyko, Lemko, Hutsul, and Transcarpathian schools.
Figure 41:
Figure 42:
The Boyko school of folk architecture construction located in the North Carpathian region of Ukraine and Southern Poland [25] is defined by the use of a linear tripartite plan of stacked log construction, topped by three multi-tiered roofs that can either be in pyramid or dome like in shape [25, 36, 40]. Though the middle one is often the highest, dome height may slightly vary depending on the region, age, or sub-style of the church. The most noticeable characteristic of Boyko churches is often its multi-tiered towers, in an often stepped pyramid fashion, where each step, referred to as a “zalom” in the Ukrainian language, is angled upward to allow for water runoff (Figure 44, 45). Boyko churches might incorporate a single zalom that breaks the dome into 2 stacked portions, or up to 7 or 8 in its most dramatic form reminiscent of multi-tiered Japanese pagodas. However, some Boyko churches may include just a middle, two, or even a domeless configuration depending on the needs or economic requirements of the community. As a result of this inconsistent dome configuration, Boyko style can often be difficult to identify. These churches can also vary in height, being either narrow and tall or short and squat. They also incorporate a lower skirt roof, called an “opasanya” that protects the exposed log walls and stone foundation from rain and snow damage. According to the 2011 UNESCO report, only 70 wooden churches of Boyko style are left [36].
Figure 43: Location of Boyko Architectural region according to Y. Taras [25]. Diagram by author.
Figure 46: Interior of the Church of the Intercession(1750) in the village of Bukovets Ukraine. (opposite) Image from Maxim Ritus.
Figure 47: Miscellaneous Boyko churches in various sub-styles. Images from Maxim Ritus.
Like the Boyko designs, churches built in the Lemko School in the Northwest Lemko cultural zone also incorporate the linear tripartite plan and three-tower configuration. However, rather than three multi-hipped pyramidal domes, Lemko designs replace them with three tall domineering towers, with the tallest, widest and bulkiest belltower over the narthex entry space, with the remaining two sequentially decreasing in size as they move towards the rear of the church. The last two towers are significantly narrower, and all three are topped with a series of stacked on ion-shaped volumes that decrease in size as they move towards the top [25, 36, 40]. This cascad ing tower configuration adds to the general elegance of Lemko churches and is often considered their main defining morphological feature (Figure 49, 50). However, Lemko sub-style designs vary greatly in terms of number of roofs, tower heights, lengths, and roof shapes. This variation is so great in fact, that the three towers completely disappear, making it difficult to tell some Lemko churches apart from Transcarpathian ones. According to the 2011 UNESCO report, only 70 wood en churches of Lemko style are left in Ukraine & Poland [36].
Figure 51: Interior of the Church of the Protection of the Mother of God (18th century) in the village of Wołowiec, Poland (left) Image from www.szlakswt.pl/wolowiec
Figure 52: Miscellaneous Lemko churches in various sub-styles. Images sourced from Google Maps.
The primary morphological features of Transcarpathian school churches can be distinguished by the emphasis of a single tower of varying heights over the narthex of the church. Followed by a single or two stepped gable roof that covers the remaining spaces of these tripartite-planned church [25, 36, 40] (Figure 54, 55). However, due to the proximity and overlapping Boyko and Lemko region in the northern portion of Transcarpathia, certain sub-styles of Transcarpathian churches have adopted aspects of both, for example, churches within the Kraynia-Svalkyasko-Plo skiv region, which contain both Lemko and Boyko morphometric features [25]. Other variants incorporate baroque and gothic features and extremely tall towers and higher nave and sanctuary spaces with double roofs, most notably built in the Southern end of the Ukrainian Carpathian re gion and those within the Marmures region of Romania [25].
Figure 54: Church of the Holy Spirit (1700) in the village of Huklyvyi, Ukraine (above). Image from Maxim Ritus.
Figure 55: Typical Transcarpathian elevation, section and plan. (below) Drawings from M. Dragan.
Figure
Differing from all other styles, churches built in the Hutsul school, which are primarily located in the Southern Ivano-Frankivs’k of Ukraine, incorporate the Greek-style cruciform rather than tripartite plan configuration [25, 36, 40]. This is achieved by widening the nave or adding rooms on either side. In combination with an omnipresent central dome, flanked by pitched roofs or sometimes two or four smaller domes, Hutsul churches are often easy to differentiate from Boyko, Transcarpathian and Lemko styles (Figure 59, 60). However, some sub-styles like within the Pokut school have shorter nave projections and as a result, far more subtle cruciform plans [25], thus making them difficult to differentiate from single-domed Boyko churches found within the Bole hivsko-Dolynsk-Pereginsk, or Sambir-Dolynsk region [25]. According to the 2011 UNESCO report, only 150 wooden churches of Hutsul style are left in Ukraine [36].
Figure 61: Interiora of Hutsul church in the village of Kryvorivnia, Ukraine.
Figure 62: Miscellaneous Transcarpathian churches in various sub-styles. Images sourced from Maxim Ritus.
Furthermore, within each school, there are countless sub-style and even sub-sub-style variations that might have dras tically different or multiple overlapping shared characteristics with one another [25]. There are many reasons for this style diversity, but many sources suggest the following. First, Carpathian wooden churches were not designed according to pre-conceived technical plans or drawings of any sort. Rather, they were conceived verbally and locally by both the craftsmen who built them and the religious members and local congregation who would attend them. Furthermore, as books or drawings of existing churches were scarce or nonexistent, church designs were often based on the visual style of neighboring churches in nearby towns [25]. As a result, this gave rise to unique style groups that tended to cluster among smaller communities that were relatively isolated together within smaller geographic regions such as within certain valleys, or along certain rivers or road networks [25]. Furthermore, as community needs, traditions, cultures and beliefs changed from region to region, so did their demands, aesthetic aspirations and spatial requirements for church architec ture, which led to the development of differing styles. However, as drastically changing traditional styles was generally avoided, church styles evolved at a very slow pace, suggesting perhaps why and how these unique sub and sub-sub styles are still present even within recent designs today [25].
In addition to a locally inspired design process, craftsmen themselves also helped to develop regional and sub-re gional styles of their own. As design and construction decisions were primarily carried out on site, the preferences, skills, tendencies and building styles of individual craftsmen led to further differentiations within the regions they worked within [25]. As a result, craftsmen, though their identities are often unknown [41], left a trail of unique churches that showcased their own architectural style variation and interpretations, therefore further enriching the overall typology with clusters of smaller sub-style groups where they worked.
Overall, a rich architectural typology which has diverse variations based on certain styles are observed in Car pathian Wooden churches (Figure 63). The subtle taxonomy of the churches shows the potential of using Deep Learning to analyze their form. As the single typology is in fact, made up of a complex interconnection of smaller formal sub-styles defined by cultural regions and sub-sub-styles, defined by communities and craftspeople, DL might help us identify these overlapping and blurry boundaries, suggesting perhaps, new interconnections, relationships, and differences.
Figure 63: Miscellaneous church substyles (above). Drawings from M. Dragan.
Figure 64: Master Carpathian builder of folk architecture Vasyl Turchynyak beside a wooden church model. (opposiute) Images sourced from www.na-skry zhalyah.blogspot.com/2016/02/blog-post_18.html
Though drastically ranging in form, Carpathian wooden churches do infact share several architectural similarities. This includes the use of timber as a primary building material, the integration of horizontal log construction, the application and showcase of masterful carpentry techniques, and the integration of a tri-partite plans as a core basis for overall geometric configuration. As shown in Figure 66, tripartite plans are composed of three quadrilateral or rectangular spaces linearly connected in a row beginning with the narthex, or small entry space, the nave where the congregation sits, and the private and smaller sanctuary space which is only accessed by the clergy and is traditionally located behind a wooden screen called an iconostas. Though 5 partite, 7 partite and 9 partite churches are prevalent, the tri-partite plan configuration can still be found in nearly all churches, with additional spaces, or partites typically radiating outward symmetrically from the central tripartite plan and are often associated with Hutsul style churches.
nave
narthex
sanctuary
Figure 65: Elevation, plan and axometric drawing of the wood construction technique of a typical Gothic-style church (opposite) Alexandru, B [65]
Figure 66: Axonmoteric diagram illustrating the typical tripartite plan configuration within a Boyko style church (above). Diagram by author
Church forms are not always static however, and can often be changed over time through additions and modifications. For ex ample, an early 13th century stone church in the Marmures region of Northwestern Romania was significantly altered twice during its lifespan. The first change took place in 1509 when a belltower was added and the second in the mid 18th century when addition al butresses and a large narthex space was built at the front of the church. Though these changes were likely done to address new functional requirements or significant structural problems, other types of changes can be carried out for less necessary aesthetic reasons. An example of this can be found in the small village of Chorna Tysa in Western Ukraine where their rare Hutsul-Baroque church was drastically “russified” by replaceing it’s culturally unique architectural elements with foreign features such as onion domes, additional towers, and brightly coloured plastic cladding. Such “russification” of historic Ukrainian folk churches has been and continues to be a common occurance, with many calling for a halt to this arguably destructive practice.
Figure 68: Images showing the exterior modification of the Church of the Assumption (1836) in the village of Black Yew, Ukraine. Before (left top), and after (left bottom). Top image from Mykhaillo Syrokhman’s personal archive. Below image sourced from https://artroad.digital/vtracheni-derev-ianitserkvy-karpat/
Such additions and modification remind us that church forms should not always be as sumed to be static or in their “pure” or original state. Though only the most recent forms of churches have been used as data for this research, including their previous forms in the future might drastically change the way in which they are encoded and understood.
Endemic to the region, the churches of the Carpathian Moun tains have become sacred and irreplaceable objects of cultural significance to both the cultural groups that built them and to the countries they are located within. As a result, efforts have been made to preserve, repair, and protect them for future generations. This was reflected globally in 2011 when 16 of these churches were designated as UNESCO world heritage sites [36]. Recogniz ing and preserving these churches is especially important given that the vast majority have been destroyed or dismantled due to both world wars, destructive ethnic and political agendas, arson, or periods of general neglect. From the thousands of churches that once existed only a small percentage survive today, with an estimate of 5-8 lost each year [37]. This loss becomes startlingly apparent when considering that out of the 689 wooden churches that existed in 1939 within the Eparchy of Przemsyl, a small his toric region slightly smaller than Delaware, the second smallest state in the United States, nearly 350 were lost by 1989, with even less still standing today [38].
Figure 70: Hand painted icons on the interior iconostasis, or gateway to the sanctuary within the Church of the Exaltation of the Holy Cross (1613) (left). Image from Maxim Ritus.
Figure 71: A Lemko church in a state of decay. Image from Iwanusiw, O. [66].
Since the start of the full-scale invasion of Ukraine, at least 270 churches have been dam aged or completely destroyed. Among them are many of the oldest examples which sur vived World War II. According to Kateryna Goncharova, a specialist in Ukrainian heritage from the World Monuments Fund, about “two churches were damaged each day” at the beginning of the invasion in 2021. According to Volodymyr Zelensky, Ukraine’s current president, “Russia is deliberately and systematically destroying Ukraine’s cultural and his torical heritage as well as social infrastructure, housing, and everything necessary for nor mal life”. Though churches in Eastern Ukraine are currently at the highest risk, any part of the country can be affected with serious damage or destruction as war can be unpredict able and missile targets cannot be known in advance. Thus, digitally preserving both the churches themselves through 3D reconstruction techniques and their stylistic “essence” by means of DL-encoding methods becomes increasingly important to help preserve these important and irrefutably distinct sacred objects of Ukrainian architectural folk heritage.
Figure 73: All Saints Shrine burning after Russian bombardment of Sviatogorsk, Eastern Ukraine (above). It was fully destroyed. Image from https://culturecrimes.mkip.gov.ua/
In order to compare our DL form analysis results with existing research, it is important to briefly describe the existing landscape of Carpathian wooden church research and how their morphometric relationships have been described over time using traditional analytical tools and methods.
To begin, the formal study of Carpathian wooden churches has a long, yet sporadic history beginning in the middle of the 19th century and continuing to this day [25]. Although these churches were mentioned as far back as the 10th cen tury by Arab traveler Al-Mas’udi in the survey “Notice of Houses Revered by the Slavs”[25], and sporadically throughout the 17th century, by historic figures such as the Patriarch Maraii and as objects of attention by famous Slavic painters Atahanasius Klyrik and Yosyp Hochemskyi [25], they remained a side note to serious academic inquiry.
However, as the culmination of national liberation movements, the rise of European Romanticism and an interest in studying one’s own history and culture arose during the mid-19th century [25], so did interest in these seemingly “exotic” and “folk-costumed” cultures and their peculiar looking churches that dotted the hillsides amongst the small and remote villages of the Carpathian Mountain region. As such, a host of scholars from all countries that bordered on these regions began the first surge in academic interest in the subject [42]. For example, “within merely two decades, a host of Czech scholars studied all aspects of Rusyn [Carpathian] culture and produced a remarkable number of publications, many of which continue to be of value today” [42]. The most significant publications of this period such as V. Miskovskii’s “Wood en Churches in the Carpathians” [43] published in 1880, and in publications by researchers T. and K. Moklovsky began to describe the overwhelming variety of church forms that occupied this region through artistic drawings, paintings, mapping of style group boundaries and locations, and comparative technical illustration of plans, elevations, detail drawings of various church styles [25].
Figure 75: Paintings of wooden churchs from V. Miskovskii’s “Wooden Churches in the Carpathians” publication, 1880. (above)
Figure 76: Drawiongs of wooden church plans, perspectives and details by K. Moklovsky (opposite)
Figure 77:
Figure 78: Portrait of V. Scherbakivsky. Image sourced from Wikipedia. (above right)
Figure 79: Images of various church styles organized per row from V. Scherbakivsky’s 1913 publication “Ukrainian Art”. (top and opposite across spread)
During the 20th century, scholars shifted their focus towards the iden tification, classification and organization of these churches into their primary groups according to morphological characteristics, as well as construction techniques, and other non-material / cultural influences. Early work that laid the foundation of this generalized understanding of church form patterns, includes publications by V. Scherbakivsky, who first described and defined the Boyko church style in 1913, V. Zalozetsky, who defined the four main church types and their general distribution in the Transcarpathian region in 1926 [25], and M. Dra gan’s 1937 publication “Ukrainian Wooden Churches: Genesis and Development of Form” [44].
After a lull in interest during and after the two world wars, lasting until the early 1960’s, and again throughout the 1980’s during Soviet rule, more recent academic work tended to focus on characteris tics of even smaller sub-regions and sub-groups of churches such as David Buxton’s 1981 publication “The wooden churches of Eastern Europe : an introductory survey”, M. Syrokhman’s “Churches of Ukraine. Transcarpathia” [45] where he focused on churches that reside within that region, or R. Sulyk’s “Wooden Church construction in Stryi Region” [46], R. Brykowski’s “Wooden Church Ar chitecture on the Crown Lands of the Polish-Lithuanian Commonwealth” [47] where he illustrated where church sub-types were clustered within a specific region and Alexan-dru Babos’s more recent 2004 publication “Tracing a Sacred Building Tradition: Wooden Churches, Carpenters and Founder in Marmures until the turn of the Century” where he diligently described the various particularities and sub-types of Marmures (Southern Transcarpathia) church types through technical drawings and maps that delineated major style locations and boundaries [48].
Figure 80: Early map of church types over broad territories. From D. Buxton’s 1981 publication”The wooden churches of Eastern Europe: an introductory survey”. (above) [40].
Figure 81: Drawings that explore the interesting Slovak Lemko churches which mix both Boyko and Lemko styles. D.Buxton [40]
Figure 82: Yaroslav Taras begins to map out, define, and coalesce in detail, the varying styles of wooden churches and their relationships to one another within the entire Carpathian mountain region. [25] Individu al illustrations by Y. Taras, but compiled, colorized and rearranged by author.
Most recently, academics like Yaroslav Taras [24, 25, 34] and Galyna Shevtsova [33], have attempted to unite this complex and largely fragmented research landscape by developing broader and more en compassing theories and surveys regarding the entire landscape of churches variation, including their genesis and development. While others, such as Mykhailo Syrokhman focus on uncovering and remem bering lost churches and their histories through recent book publications and research projects while promoting the protection of churches that still stand [63]. Nevertheless, Taras laments that research has also hit a roadblock due in part by the “lack of comprehensive studies of building types using the car tographic method[s], [for example], drawing hundreds of plans, facades and putting them on thematic maps in order to identify the geography of the spread of characteristic architectural solutions and the boundaries of architectural and cultural districts” [25]. However, as DL-learning techniques provide the means to potentially do this, I believe contemporary tools leveraging AI may help experts to continue this history of research, conduct new studies, make interesting discoveries, and gain new insight into the folk culture of Carpathian wooden churches.
This thesis intends to show that insightful morphological relationships among our dataset of 313 Carpathian wooden church es can be revealed using contemporary deep learning (DL) techniques and reflects on specific ways these analyses might enrich existing architectural scholarship and expert knowledge on the subject. However, to set itself apart from other work, this thesis argues that findings acquired from DL-based “distant reading”[6] techniques are more thoroughly understood when compared against the findings of traditional “close reading” [6] approaches and past scholarship. However, as related investigations using DL has almost exclusively used extremely large datasets that have rarely been analyzed using traditional approaches due to their sheer size, such a comparison has often been difficult or impossible to do. As a response, my thesis uses a small dataset of a previously studied building type, being the wooden churches of the central Carpathian region, in order to make this comparison and demonstrate how DL-based findings can enhance our understanding of architectural form most efficiently when taking into consideration the findings acquired through more traditional approaches.
To begin, I will compare DL-informed results with conventional studies and contemporary scholarship that have uti lized traditional analytical methods and demonstrate that there is correlation between the way in which these two differing approaches have grouped churches in terms of similar form. Furthermore, I will demonstrate how deep learning allows us to explore and quantitatively compare the complex forms of hundreds of churches simultaneously according to 128 indepen dent variables as opposed to traditional techniques that can only compare a handful of buildings and a limited number of architectural features at a time. I will then show how DL techniques can help to identify more difficult to identify groups of churches that share similar forms according to more general or more specific architectural features by altering cluster size. This allows both large primary, medium secondary, and small tertiary church style groups and architectural form patterns to be identified. Additionally, I will provide evidence that DL-techniques can help to identify and located clusters of harder to recognize endemic micro-styles that have developed within specific isolated geographic regions, such as in certain valleys or
along certain rivers. Furthermore, I will demonstrate how the specific architectural features that define a particular style re gion transition into those of neighbouring regions in an slow, rather than abrupt manner. This is evidenced by the numerous hybrid style churches that incorporate the defining architectural features of two or more style regions at once. This often oc curs at the border areas between two or more style regions. Finally, I will offer a path for DL-based form analysis techniques to be put in conversation with traditional architectural studies in order to help expand and enhance our understanding of Carpathian wooden architecture.
In addition, my thesis intends to offer a number of useful technical contributions to both the field of architectural form-analysis and to the research of Transcarpathian wooden churches. First, it will demonstrate a method to build a custom 3D dataset of a non-western, rural, and “niche” building type using the NeRS technique [23] that converts sparse imagery into digital three-dimensional models. Second, it will show how similarly small and custom datasets can be successfully augment ed in size in order to sufficiently train DL-models for 3-D pattern recognition. As a result, this research will likely be the first of its kind to apply DL-based form analysis approaches to a custom small-scale 3D dataset of vernacular architecture. Thirdly, it will present a multi-modal approach for analyzing latent space distributions by incorporating both DL-oriented approaches, such as Rhee’s method of clustering and identifying typical and least-typical form [64], and traditional approaches such as reference to cultural-zoning maps, architectural drawings, style diagrams, and other existing research material [24, 25].
Furthermore, as the author is of Ukrainian heritage whose descendants are from Bukovina, a unique historic region with its own distinct style of Carpathian wooden church, this thesis is furthermore a means to learn about and bring aware ness to the culturally significant folk architecture of his ancestral roots.
To extract the morphological patterns of Carpathian wooden churches, our DL model had to be provided with, as input, a large dataset of digitally represented churches including as many primary and sub-style church variations as possible. Due to the lack of pre-existing digital 3D data, we created a custom dataset of hundreds of churches. Rather than digitally modeling each church by hand, a computer-vision technique called Neural Reflectance Surfaces (NeRS)4 [23] was used to convert 2D images of hundreds of churches into 3D reconstructions.
The dataset used in this paper was built upon 331 original 3D building forms representing the 3D re-constructions of 331 existing and non-existing — i.e., dismantled or destroyed — historic wooden churches generally located in the Car pathian region of Eastern Europe. In order to meet the data size requirements of the DL model, the dataset was augmented to 5,627 buildings using random rotation and scaling. This one-of-a-kind 5,627 3D church dataset was custom made by the author and represents the first of its kind for this building type and likely the first of any building type to be reconstructed from sparse 2D imagery in this particular way. Rather than homogenous in appearance, the churches within this dataset represent the rich heterogeneous mixtures of styles that define the Carpathian wooden church building type.
The key advantages of the data used in this paper, includes the following: To begin, the architectural data, as shown in figure 83, is in the form of generally high-quality 3D models rather than more abstracted representations such as 2D build ing footprints, images or 3D extruded building footprints. For example, both Moosavi and Cai used datasets in the form of lower representation 2D images as shown in figure 18 and 23 [20, 21]. By using 3D forms, more complex and nuanced building forms and architectural components can be encoded and learned by the DL model. For example, complex roof shapes, subtle undulations in exterior envelopes, building overhangs, and other architectural extrusions, recesses, and geo metric elements that may be more indicative of a particular architectural style.
Our data represents a unique, generally rural, and rare building type with just a few hundred examples left standing today [26]. Due to the limited availability of high-resolution niche or even common 3D data of digital building models [27], comparable work typically has used pre-existing, urban architectural data sourced from large and readily available city models [20,21]. Therefore, to the best of my knowledge, there are very limited, and likely no other existing studies apply ing DL-form analysis methods to similar types of rural and stylistically niche architectural datasets.
The reason for the under-representation of high quality rural, smaller urban, or niche 3D architectural data at scale is likely due to the cost associated with creating large datasets of 3D models as it requires a huge investment of time, labor, and money for expensive equipment [28]. Though a high-quality 3D model of a single building can be manually acquired for little to no cost by using industry standard photogrammetry smartphone apps like Bentley’s context capture software, this method is infeasible if you need to capture hundreds or thousands of buildings at a time.
After surveying existing city models, it was observed that high quality architectural data was often only available for larger urban environments of wealthier countries. Though 3D models for other urban environments may exist, they are often low resolution, highly abstracted and typically represented as simple volumetric building extrusions [27], thus providing insufficient building form detail for this or similar types of DL-based research. As for generally rural, rare and niche, building types like the Carpathian wooden churches, it is unlikely that any more than a handful of models, likely constructed by different sources, may exist Online. As a response, this thesis demonstrates how large 3D datasets of hun dreds or thousands of non-urban, rare, and niche building types from any country can be built by anyone from scratch. We additionally demonstrate how to augment smaller datasets in order to be sufficiently large enough to train a DL networks.
A plain-text CSV file also accompanies each church .obj and .mtl files and contains church specific metadata, such as church name, construction date, latitude, longitude, address, google maps link, status (standing, not standing), links to descriptions, and image source providers. As this 3D dataset of Carpathian wooden churches is the first of its kind, the authors hope that providing both the 3D model and metadata might benefit others who are conducting related research related to the Transcarpathian wooden church typology. Metadata for each individual church has been included here and can be found within the appendix section at the end of this book.
Figure 88: Meta data csv file built by author. Diagram by author.
My process to extract the morphological patterns of churches using deep learning can be partitioned into three broad stag es; the dataset construction stage, the DL model training stage, and the model analysis stage. This entire process took 7 months to complete. The dataset construction phase was the most time consuming phase and took 6 months to complete while the model training and model analysis stages were relatively quick and took just over 1 month to complete in total.
In the dataset construction stage, we needed to provide our DL model with, as input, a large dataset of digitally represented churches including as many primary and sub-style church variations as possible. Due to the lack of pre-existing digital 3D data, we created a custom dataset of hundreds of churches. Rather than digitally modeling each church by hand, a computer-vision technique called Neural Reflectance Surfaces (NeRS) [23] was used to convert 2D images of hundreds of churches into 3D reconstructions. These 313 3D models were then augmented to 5,627 models through random scaling and rotation in order to satisfy the dataset volume requirements of the model we used.
Figure 89: Project timeline. Diagram by author.
In the DL model training stage, a Variational Autoencoder (VAE) was used to analyze the 5,627 church forms within the dataset due to its superior analysis operations such as pattern recognition, clustering, and various types of classification. By implementing state-of-the-art feature extraction processes, it identified and extracted extremely complex and intertwining form patterns that would have been impossible to identify using traditional “close reading” form analysis approaches such as shape grammars, diagramming, or architectural drawing comparison.
In the final model analysis stage, DL-based results are carefully studied by using latent space clustering techniques which group churches according to similar forms. By referring to existing research, expert knowledge, and other acquired domain knowledge, both meaningful and potentially misleading morphological patterns were revealed. In this way, both macro and micro form patterns were identified helping to help reinforce existing theories and reveal new insight into the form patterns of the hundreds of wooden church styles of the Carpathian Mountain Region.
From 2D Images to 3D Models
As high resolution 3D datasets of hundreds or thousands of rural, non-western, and niche building types are largely non-ex istent, the author had to create his own custom dataset of Carpathian wooden churches. Rather than manually modeling each church in digital space from scratch, the authors chose to use a 3D reconstruction technique to convert 2D images of churches into 3D models. Though there are many other 2D image to 3D model conversion systems available such as Bentley System’s “Context Capture” [49] or Epic Games “Reality Capture” [50], these and other similar systems require hundreds of images taken within highly controlled environments with consistent lighting conditions, precise camera lo calization in order to create a 3D model. As hundreds of photos of Carpathian wooden churches with such rigid imagery requirements simply do not exist, nor was it feasible to travel to Eastern Europe take them myself, I chose to construct our dataset using a novel 3D reconstruction technique called Neural Reflectance Surfaces (NeRS) [23], which can convert just a few images taken in various lighting condition, with different cameras, and with different optical settings into “geomet rically and texturally accurate water-tight 3D reconstructions” . NeRS was developed at Carnegie Melon University in 2021 by Jason Zhang Et. al, NeRS as part of his PhD research.
NeRS Pre-Processing Image Sourcing Figure 90: Reconstruction process. Diagram by author.
Constructing a dataset of 3D churches using NeRS and then preparing it for DL analysis requires a nine-stage work flow (Figure 90): 1) a broad search for churches and acquisition of exterior church images; 2) selection of the final images showcasing all exterior sides of each church; 3) editing the images to remove occlusions, correct perspective distortions, and compensate for missing images; 4) creating image masks representing the location and outline of the church; 5) esti mating image angles between the camera and the church; 6) estimating the general dimensions of the churches (length, width, and height) ; 7) reconstructing a high-quality detailed 3D mesh object, 8) Augmenting the data from 331 churches to 5627 models, 9) converting the models to SDF voxel format.
The final dataset consists of 5627 voxel files, each being 129 kb, leading to a total dataset size of 725.88 MB
Deep Learning Preparation NeRS
To reconstruct a church in 3D, NeRS requires at least 6-8 images showing all sides of the church. As a pre-existing data base of these images does not exist, they had to be manually and painstakingly searched for and collected from a variety of sources. Over 500 churches had to be manually discovered without prior knowledge of their existence or location. The entire process took 3 weeks to complete. Over 10,000 images, representing 515 different churches were found in this way.
Figure 91: Images collected from a variety of sources to be used for church 3D reconstruction (above)
Figure 92: Location of all churches within 3D dataset. (opposite)
Figure 93: 16x9 mile search grid overlayed over search boundary. Diagram by author.
Figure 94: Church of St. Nicholas the Wonderworker (1798) in the town of Izky, Ukraine. (opposite). Image from Maxim Ritus.
The wooden churches used in this research reside within the mountainous, foothill and water-shed region of the Carpathian Mountain region of Central Europe. As this region encompasses upwards of two-hundred thousand square kilometers it quickly became apparent that searching for a collecting images of all wooden churches within this area was not feasible. Thereby, I chose the smaller central re gion of the Carpathian Mountain region as the area in which to search for churches and source imagery from (Figure 93). This smaller geographic region encompasses several unique cultural regions and as a result, includes most of the primary wooden church style variations. Some churches from outside of this region, though relatively nearby were also included in order to add further form diversity to the dataset.
The first source used to find and collect images of churches was Google maps, where a comprehensive search within the target region was carried out. In order to do this, a 16 mile by 9-mile search grid over the central Carpathian region was defined and allowed for systematic discovery, identification, and image collection. This process, which took multiple weeks to complete, identified 275 churches, therefore being the most significant source for churches and church imag es. By scanning through satellite views of the landscape while searching for the term’s “church”, “wooden church”, and their Ukrainian, Polish, Slovakian, Hungarian translated equivalents depending on the search region, many churches not provided by previous sources were located. Once a church was found on Google Maps, and if available, crowd sourced images of the building could then be acquired as screenshots (Figure 96). However, as not all churches had imagery or had insufficient image coverage of all sides, supplemental images had to be searched for and collected from other sources.
Academics who allowed me to use imagery from their own personal collections included Mykhailo Syrokhman and Olena Krushynska. Syrokhman, a professor at the Transcarpathian Arts Institute in Uzhorod, Ukraine and Ukraine’s leading ex pert on the Wood Churches of Transcarpathia provided images of churches that no longer exist, or have been inadequately documented, thus adding a unique dimension to the dataset that would have otherwise been impossible to obtain. Olena Krushynska, a Chemist at Kyiv National University, with a passion for wooden churches, had developed and continues to maintain a website for her project the “Wooden Temples of Ukraine” [51]; the most extensive catalog of wooden churches in all of Ukraine. On her website, she also meticulously documents each church location and provides a Google Map link, which was helpful as there were many churches within my dataset that were difficult to locate.
Figure 97: MyKhailo Syrokhman giving a lecture on the Lost Churches of Transcarpathia. (above)
Figure 98: Map showing area where he provided images for. Diagram by author. (opposite top)
Figure 99: An image provided by M. Syrokhman of the Church of St. Nicholas (1797) in the village of Pryslop, Ukraine. (opposite bottom)
Figure 100: The no longer existing Church of St. Nicholas (17th Century) in the village of Velyke Urmeziyevo, Ukraine. (opposite). From the photo archive of M. Syrokhman.
Figure 101: An image of a Boyko church in the town of Sukhyi, Ukraine (above). From the photo archive of M. Syrokhman.
Independent researchers include Maxim Ritus, who thoroughly documents the interiors and exteriors of hundreds of cul turally significant buildings in Ukraine through technical land-based and drone photography (Fig-ure 4-2). He records this ongoing project on a blog [52] which he continuously and frequently updates with both photographs and detailed descrip tions of new buildings on nearly a weekly basis. He graciously gave me permission to use his photographs for this work.
Figure 102: Church of St. Anne (1700) in the village of Bukivts’ovo, Ukraine. (left) Image from Maxim Ritus.
The third source used for acquiring images were independent websites that contained their own unique church image repositories. Images from this source were supplementary to the 3D reconstruction process that were not available via academics, independent researchers, or Google Maps. Some of the more helpful websites were “Castles and Churches of Ukraine” [53] a project that aims to catalog historic buildings in Western Ukraine run by Iryna Pustynikova and “The Drymba Project” [54] which is a Ukrainian tourist and local history network. One of the most helpful independent website sources was the “Pslava” project website, which is a large database that includes images, drawings and detailed descrip tions of over 70,000 historic and culturally significant monuments meticulously organized by region within various Cen tral European countries developed by Mykola Zharkykh [55].
The final source used for acquiring the most elusive church images were miscellaneous websites. These websites varied from news websites that may have published an online article on a particular issue surrounding a church, a community Facebook page where an image of religious event may have included a church in the background at the required missing angle, or a on a personal blog of someone who passed by the related building and posted a photo online.
Finally, roof plan images of each church were acquired via satellite imagery sourced from the Google Earth application (Figure 106). Rather than acquiring images from Google Map’s satellite view mode, the Google Earth app provides multiple satellite images of the same geographic locations taken at different time periods. This image variety is important as satel lite views can often be low quality or negatively affected by long shadows during certain times of the day, or have objects covered by occlusions like leaves from trees or cloud cover. Having multiple image options provides a means to compare the images and select the one with the highest detail and the least occlusions.
In total, over 10,000 individual images of 517 wooden churches were painstakingly collected in this manner (Figure 107). It is important to note that not all churches were able to be reconstructed in 3D do to insufficient or incompatible imagery.
Figure 107: Diagram showing the proportion of church images that came from various sources. Diagram by author.
Next, the images for each church needed to be pre-processed and properly prepared in five stages. In the first stage, the final images required for the 3D reconstruction process need to be selected. Though many images of each church have been collected, not all of them will be used in the NeRS 3D reconstruction method. In general, images that share as similar field of views, lighting conditions, and vertical angles, as possible should be used as they produce the best reconstructions, though this is not required given the flexibility of the NeRS approach. In total, only 8-10 images need to be included. In cluded images should show the following sides of the building. front (0º), front left angle (45º), left side (90º), back left angle (135º), back (180º), back right angle (225º), right side (270º), front right angle (315º). However, as churches are almost always symmetrical, images for only half the church need to be acquired (Figure 110). These images will be later horizontally flipped to represent the other side of the building in step 5 of the image processing stage.
In the second stage, some images were required to be altered in order to ensure their associated church was ac curately reconstructed in 3D. For example, images occasionally had to be manually editing or constructed to provide the front or rear elevations which, after much experimentation, was required by NeRS to create accurate 3D reconstructions. As images from directly in front or directly behind churches are not always guaranteed, “false” perspectives needed to be custom created to provide this angle through image warping or collaging multiple images together (Figure 109). Addition ally, some images had major occlusions that needed to be removed manually, while others sometimes had FOVs that need ed to be altered through image skewing techniques. Edits were carried out in the photo editing software Adobe Photoshop.
(top) Diagram by author.
In the third stage, object masks needed to be provided for each individual image. As there are thousands of im ages, an object detection model was used to automatically detect the church and output an accurate image mask which represents the foreground volume of the church with white pixels and the background with black pixels. Providing these images ensures that NeRS only reconstructs the church, rather than its surroundings. However, image masks that included occlusions such as plants, people, and other objects in front of the church had to be manually edited.
In the fourth stage, both the horizontal and vertical camera angle between the camera and the church was man ually recorded to a JSON file. These JSON files and their embedded angle information is necessary for the reconstruction process as it helps to determine how images correspond to the shape being reconstructed, and thus how images relate to one another.
In the fifth stage, a custom script was created to automatically duplicate images and horizontally flip them to represent the other side of the building. Horizontal and vertical meta data recorded in the JSON file is also duplicated and modified to represent the newly created and flipped images.
In the sixth and final stage, the dimensions of a shape template corresponding to the estimated approximate size of each church must be determined. A custom script was similarly produced to help expedite this process and save to the corresponding JSON file.
Input Images
Camera Optimized to Match Building Angle
Once all images have been preprocessed and their corresponding JSON files complete, the final stage of the reconstruction pipeline (Figure 113) was reached and each church was reconstructed from the images as a high-quality detailed mesh ob ject using the NeRS algorithm. To reconstruct each church a remote computing cluster containing 4 GPUs (NVidia GeForce GTX Titan X with 12 GB of VRAM) was used to expedite this process. Once completed, the reconstruction quality of each church was manually inspected.
▪ Images
[378_0, 378_1, 378_2, 378_3, 378_4, \ 378_5, 378_6, 378_8, 378_9]
▪ horizontal angle [0, 45, 90, 135, 180, 225, 270, 315, 360]
▪ vertical angle [0, 0, 0, 15, 0, 15, 15, 0, 0]
▪ template shape [0.7, 1.0, 0.4] ▪ id # 378 ▪ name “Church of St. Nicholas” ▪ year 1756 ▪ existing “yes”
Churches displaying unsatisfactory reconstructions were modified accordingly (image and image parameter modi fications) and then reconstructed again locally until satisfactory reconstruction results were achieved. Churches that could not achieve satisfactory reconstruction results were discarded. Out of the original 409 churches, 96 could not be recon structed due to insufficient imagery.
Figure 114: Images of a 3D reconstructed church (3rd column) compared to the input images (1st column). 3D reconstruction overlayed ontop of original image for comparison (middle column). Diagram by author.
The dataset of reconstructed churches includes 313 individual buildings (Figure 115, 116). The dataset were then auto matically augmented from 313 buildings to 5,627 buildings using an algorithm that made slightly different copies of each building through a series of random rotation and scaling transformations.
The dataset creation phase was hands-on and painstakingly manual. Nevertheless, it was beneficial as it helped us learn a wide range of church styles through repeated exposure. For example, the Lemko Snynskyi and Transcarpathian Velikobereznyansky-Chornogolovska sub-styles could be easily differentiated according to their slightly differing tower and roof designs even though they seemed identical prior to the dataset creation phase(Figure 182). As such, we obtained a more intuitive understanding of both the complex and nuanced church styles and overall topography of form variation. Furthermore, this deeper understanding allowed us to better interpret findings and identify form patterns and relationships that might have been missed. Rather than being an unnecessary task, the manual data creation phase became an extremely valuable exercise in data literacy and was integral to properly evaluating, understanding, and interpreting results.
In order to sufficiently train the DL network and extract a reasonable latent space distribution, where latent space is a representation of abstracted data where similar data points are clustered closer to gether, the dataset needed to be augmented from 313 buildings to 5,627 buildings. Data augmenta tion is common technique if the dataset is insufficiently large to train a DL-model and helps prevent overfitting [32]. Overfitting is when a model begins to memorize hyper-specific features of the data, rather than more useful general features of the data that might help better describe it as a whole. To avoid overfitting, the dataset was augmented to be 17X its original size by making 17 copies of each building, with each copy being slightly different from the next through a series of transformations such as rotation and randomly scaling. To expedite this process, a custom script and data pipeline was created in Grasshopper, a parametric design software used to create and manipulate 3D geometry.
5,008 copies
17 copies of the original model that slightly vary in size & orientation. Expedited using a custom algorithm and grasshopper script.
The 3D buildings that make up our dataset are represented as a collection of vertices, vertexes, vertex normals, and faces in .obj format; a simple and widely used data-format that represents 3D geometry alone [29]. Each .obj file is also accompanied by two texture files, the first being an image of the building texture in .png format, and the second being a .mtl file that specifies the material properties.
As most DL models were developed to encode imagery represented by pixels, the 3D .obj files were converted to voxels in order for them to be compatible to this underlying image-oriented archi tecture. This is because voxels are a format which represent 3D objects in a similar way that 2D imag es are represented by square pixels. Voxels, however, represent 3D forms as a collection of 3D pixels organized within a 3D grid of a user-specified size. For this paper, 3D church models were converted to 32x32x32 voxels using signed distance field (SDF) [30], for continuous representation within voxel space. With normal voxels, each voxel within the 32x32x32 grid, representing 32,768 individual vox els, is represented by a 0 or 1. 1 meaning that the voxel overlaps with the given geometry, and 0 if it does not. SDF however, assigns a float point (number with decimals) rather than binary point (0 or 1) to the voxel, thus not only indicating whether a voxel has intersected, or is within the given geometry (positive numbers indicate that the voxel is inside the 3d volume and negative numbers indicate that the voxel is outside), but also that voxel’s distance to the surface of the given geometry [30] (fig. 2-2). This added information allows SDF voxels to achieve a much higher representational accuracy than typical voxels.[30]
Figure 118: 3D hurch representation in various formats. (top) Diagram by author.
Figure 119: Illustration showing how SDF voxels are formed. (bottom) Diagram by author.
The final augmented dataset for model training included 5627 voxel files, each being 129 kb, leading to a total dataset size of 725.88 MB
Church of the Holy Spirit Huklyvyi, Zakarpattia Oblast, Ukraine circa 1700
Church of St. Nicholas Nyzhnya Apsha, Zakarpattia Oblast, Ukraine 1604
Church of St. arch. Michael Smozhe, Lviv Oblast, Ukraine 1874
Church of St. Michael Sobovich, Zakarpatia Oblast, Ukraine 1745
Debeslavtsi,
Church of St. Spirit Kotel’nytsya, Zakarpattia Oblast, Ukraine 1760 Lysovychi,
Church of St. Panteleimon Honyatychi, Lviv Oblast, Ukraine Built 1872
Figure 120: Illustration of the total dataset size compared to the number of orginal 3D reconstructed churches. Diagram by author.
Final Dataset: 5,627 3D churches
Recently, the study of architectural form has experienced a surge in interest, due in part, by the advancements in Deep Learning (DL) based visual pattern recognition tools using Convolutional Neural Networks (CNN) like Generative Adver sarial Networks (GAN) [18] or Variational Autoencoders (VAE) [19]. As a definition, DL-learning is an algorithmic meth od used to extract complex statistic-based patterns from large collections of data. In this case, our DL network “learns” the range of church styles in our dataset by analyzing thousands of examples at a time in order to find reoccuring data patterns, here, being reoccuring church styles such as Lemko or Boyko. Then, by encoding these style patterns as unique sets of 128 numeric values, where each value represents a particular feature of that style, for instance bell-tower height, they can then be compared to one another according to the similarity or differences of these numbers. Compared to more traditional methods, which take time, are often manual, and can analyze just a handful of buildings at once, these new statistic-based techniques can be applied to analyze the styles of thousands or even millions of buildings at a time. And after a relatively short processing period, provide the means to explore and identify both complex and difficult to detect morphological patterns that permeate through very large collections of buildings or other urban data. For example, in his 2019 paper “Context-Rich Urban Analysis Using Machine Learning: A case study in Pittsburgh, PA” [64], Jinmo Rhee uses these methods to identify the various types and locations of urban form patterns throughout the entire city of Pittsburgh, Pennsylvania by analyzing tens of thousands of its building and urban footprints. By leveraging the analytical power and capabilities of DL, we can now obtain results that might have taken years to complete via traditional methods.
The neural network architecture chosen for form analysis is a variational autoencoder (VAE) [19]. Similar to GANs [18], VAE are among the most widely studied and prominent generative models used within the field of deep learning for tasks like image synthesis [56] and novel 3D shape generation [44]. Though traditional VAE’s do not achieve the sharpness and clarity in novel generations as compared to GANs [57], they can yield a robust manifold in a latent space through accurate encoding and robust mapping of visible variables (x) to latent variables (z) [19]. Here, a manifold can be understood as “groups or subsets of data that are ‘similar’ in some way. [58] While latent space can be thought of as “simply a represen tation of compressed data in which similar data points are closer together in space” [58]. As this thesis focuses on shape analysis, rather than novel generation, a VAE model (Figure 122) was selected due to its superior analysis operations such as pattern recognition, clustering, and various types of classification, all of which require robust manifolds in latent space.
128 float values
original 3d church VAE model reconstructed 3d church encoder decoder encoded 3D Church [ 3.3, 3.4, 1.3, 2.6, 2.9, 1.3, 2.2, 2.0, 1.1, 2,4. 9,4. 3.0, 3.4, 1.9, 1.1, 1.4, 2.0, 3.7, 1.4, 3.7, 3.7, 2.4, 2.1, 3.2, 2.7, 4.5, 1.0, 4.2 , 2.7,4.0, 1.4, 4.3, 4.1, 3.1, 4.9, 3.8, 4.5, 3.1, 1.4, 4.9, 1.4, 4.5, 4.5, 2.6, 3.2, 1.5, 1.6, 4.0, 2.6, 4.2, 4.8, 2.5, 1.9, 3.7, 4.3, 3.4, 2.5, 1.8, 4.0, 3.0, 3.1, 1.2, 2.1, 3.1, 4.5, 4.9, 2.5, 2.5, 4.4, 1.8, 5.0, 2.9, 4.5, 1.4, 1.5, 2.9, 3.5, 3.9, 2.8, 3.8, 2.1, 1.8, 1.1, 1.1, 3.9, 2.0, 3.8, 4.3, 2.8, 2.7, 3.9, 4.3, 4.3, 4.7, 1.8, 1.0, 3.2, 2.4, 3.0, 4.9, 2.6, 3.5, 3.0, 2.5, 3.5, 2.0, 4.2, 3.0, 5.0, 1.9, 4.9, 3.0, 1.5, 3.9, 4.9, 1.8, 3.0, 2.1, 1.3, 3.3, 4.9, 2.7, 2.9, 2.2, 1.2, 3.6, 3.9, 2.7 ]
Figure 122: Illustration of a Variational Autoencoder (top). Original model architecture image from http://www.cs.cmu.edu/afs/cs/user/bhiksha/WWW/ courses/deeplearning/Fall.2018/www/recitations/rec10.vae.pdf. Diagram modified and added to by author
Figure 123: Diagram of 128 vector z-value which, in its entirety, represents the outward appearance of a single church (bottom). Diagram by author.
Generally, VAE consists of two neural networks, the encoder (E), which converts input data into an encoded rep resentation, and the decoder (D) which reconstructs the data from the encoded representation [19]. Together, VAE aims to “jointly [learn] deep latent-variable models and corresponding inference models using stochastic gradient descent” [58]. In other words, they aim to fit a probability distribution to the features of the data. When reconstructing these data features, there is a loss score which, in general, is the difference between the original and the reconstructed data. This loss score is then used to update the parameters of the distribution to better fit, or match the original data features, thus decreasing the loss.
To begin the process, the encoder abstracts high-dimensional data (x), in this case a 3D church model, into a low-dimensional vector (z), represented by numeric float values between 0 and 1 [19]. This data abstraction yields a dis tribution over a reduced dimensional latent space, with the number of dimensions determined by the number of numeric float values (also called latent vectors) chosen. For this project, I chose to use a 128-dimension latent space, thus our VAE attempts to accurately represent each church as a series of 128 individual float values, with each value representing a unique morphological characteristic identified by the model.
Next, the decoders job is to then reconstruct the original object as accurately as possible by sampling the original data from the latent space. If the encoder abstracts data poorly, a rough or inaccurate decoded output will result. This reconstruction loss (L), or the difference between the original data and reconstructed data, is finally calculated by taking the sum of the expected log likelihood (E) (reconstruction loss) and the prior term (KL loss), which is then backpropagated through the network in order to update the networks weights and improve the encoder [19].
This iterative process of encoding, decoding then improving through backpropagation occurs over and over until the reconstruction error is reduced as much as possible and a satisfactory reconstruction output is achieved. However, unlike traditional Autoencoders which tend to overfit and produce irregular latent spaces that make data analysis or gen eration extremely challenging, VAEs address this by regularizing the encoder using a prior over the latent distribution p(z) [19]. Due to the stable and regular latent space and its advantages for analytical tasks, a VAE model was chosen to conduct all analytical experiments on our dataset. [19].
To train the VAE model on our 5,627-building dataset I used a computer custom built for deep learning operations which has the following specification: An Intel Core i9-10920X CPU @ 3.5 Ghz, a GIGABYTE X299X Aorus Master Motherboard, one NVidia GeForce RTX 3080 Ti GPU with 12 GB of RAM, and 128 GB of memory. Training was carried out using a VAE model for 3000 epochs over a period of just over 4 hours. To adequately tune the model, the following hyper parameters were used: a batch size of 32, a single learning rate of 0.00005 with Adam optimizer, and a latent space size of 128.
1.8 epochs
1.6 1.4 1.2 1 0.8 0.6 0.4 0.2
0 500 1,000 1,500 2,000 2,500 3000
losses 0 epochs 5 epochs 25 epochs 75 epochs 150 epochs 250 epochs 1,000 epochs 3,000 epochs g.t. Figure 125: Model training diagram according to epochs and loss score with reconstruction images of church at various training stages. Diagram by author
Data was split into a 5,065-object training set and 562 object test set using a standard 1:9 ratio. At approximately 330 epochs, the training and test errors intersected leading to slight overfitting and a final train - test error difference of 0.02. The final training loss was 0.34 and the final test loss was 0.32 suggesting only negligible overfitting (Figure 125). Convergence occurred just after 900 epochs. Reconstruction of the encoded data is show in Figure 126 and displays satisfactory results.
original .obj object ground truth SDF voxel
VAE Model (3,000 epochs) reconstructed .obj object
Encoder Decoder z-value
Encoder Decoder z-value
Encoder Decoder z-value
Investigating Encoded Church Styles
Once training was completed and the model could satisfactorily encode and decode the dataset, I was then able to analyze whether any form patterns were learned by the model by clustering churches according to similar morphology, which here are represented by their unique z-values. The framework for architectural form clustering and investigation as described in the next sections was developed by Jinmo Rhee and demonstrated in his 2019 paper “Context-rich Urban Analysis and Type Investigation in the Form of High-rise Building Using Deep Neural Network” [64].
original .obj object SDF voxel (g.t.) VAE Model (pre-trained)
Encoder Encoder Encoder
Scatter Plot (location of x,y values) Z-Value (128 values)
[ 3.3, 3.4, 1.3, 2.6, 2.9, 1.3, 2.2, 2.0, 1.1, 2,4. 9,4. 3.0, 3.4, 1.9, 1.1, 1.4, 2.0, 3.7, 1.4, 3.7, 3.7, 2.4, 2.1, 3.2, 2.7, 4.5, 1.0, 4.2 , 2.7,4.0, 1.4, 4.3, 4.1, 3.1, 4.9, 3.8, 4.5, 3.1, 1.4, 4.9, 1.4, 4.5, 4.5, 2.6, 3.2, 1.5, 1.6, 4.0, 2.6, 4.2, 4.8, 2.5, 1.9, 3.7, 4.3, 3.4, 2.5, 1.8, 4.0, 3.0, 3.1, 1.2, 2.1, 3.1, 4.5, 4.9, 2.5, 2.5, 4.4, 1.8, 5.0, 2.9, 4.5, 1.4, 1.5, 2.9, 3.5, 3.9, 2.8, 3.8, 2.1, 1.8, 1.1, 1.1, 3.9, 2.0, 3.8, 4.3, 2.8, 2.7, 3.9, 4.3, 4.3, 4.7, 1.8, 1.0, 3.2, 2.4, 3.0, 4.9, 2.6, 3.5, 3.0, 2.5, 3.5, 2.0, 4.2, 3.0, 5.0, 1.9, 4.9, 3.0, 1.5, 3.9, 4.9, 1.8, 3.0, 2.1, 1.3, 3.3, 4.9, 2.7, 2.9, 2.2, 1.2, 3.6, 3.9, 2.7 ]
[ 3.3, 3.4, 1.3, 2.6, 2.9, 1.3, 2.2, 2.0, 1.1, 2,4. 9,4. 3.0, 3.4, 1.9, 1.1, 1.4, 2.0, 3.7, 1.4, 3.7, 3.7, 2.4, 2.1, 3.2, 2.7, 4.5, 1.0, 4.2 , 2.7,4.0, 1.4, 4.3, 4.1, 3.1, 4.9, 3.8, 4.5, 3.1, 1.4, 4.9, 1.4, 4.5, 4.5, 2.6, 3.2, 1.5, 1.6, 4.0, 2.6, 4.2, 4.8, 2.5, 1.9, 3.7, 4.3, 3.4, 2.5, 1.8, 4.0, 3.0, 3.1, 1.2, 2.1, 3.1, 4.5, 4.9, 2.5, 2.5, 4.4, 1.8, 5.0, 2.9, 4.5, 1.4, 1.5, 2.9, 3.5, 3.9, 2.8, 3.8, 2.1, 1.8, 1.1, 1.1, 3.9, 2.0, 3.8, 4.3, 2.8, 2.7, 3.9, 4.3, 4.3, 4.7, 1.8, 1.0, 3.2, 2.4, 3.0, 4.9, 2.6, 3.5, 3.0, 2.5, 3.5, 2.0, 4.2, 3.0, 5.0, 1.9, 4.9, 3.0, 1.5, 3.9, 4.9, 1.8, 3.0, 2.1, 1.3, 3.3, 4.9, 2.7, 2.9, 2.2, 1.2, 3.6, 3.9, 2.7 ]
[ 3.3, 3.4, 1.3, 2.6, 2.9, 1.3, 2.2, 2.0, 1.1, 2,4. 9,4. 3.0, 3.4, 1.9, 1.1, 1.4, 2.0, 3.7, 1.4, 3.7, 3.7, 2.4, 2.1, 3.2, 2.7, 4.5, 1.0, 4.2 , 2.7,4.0, 1.4, 4.3, 4.1, 3.1, 4.9, 3.8, 4.5, 3.1, 1.4, 4.9, 1.4, 4.5, 4.5, 2.6, 3.2, 1.5, 1.6, 4.0, 2.6, 4.2, 4.8, 2.5, 1.9, 3.7, 4.3, 3.4, 2.5, 1.8, 4.0, 3.0, 3.1, 1.2, 2.1, 3.1, 4.5, 4.9, 2.5, 2.5, 4.4, 1.8, 5.0, 2.9, 4.5, 1.4, 1.5, 2.9, 3.5, 3.9, 2.8, 3.8, 2.1, 1.8, 1.1, 1.1, 3.9, 2.0, 3.8, 4.3, 2.8, 2.7, 3.9, 4.3, 4.3, 4.7, 1.8, 1.0, 3.2, 2.4, 3.0, 4.9, 2.6, 3.5, 3.0, 2.5, 3.5, 2.0, 4.2, 3.0, 5.0, 1.9, 4.9, 3.0, 1.5, 3.9, 4.9, 1.8, 3.0, 2.1, 1.3, 3.3, 4.9, 2.7, 2.9, 2.2, 1.2, 3.6, 3.9, 2.7 ]
[4, 8] [-7, -6]
[21, 5]
Figure 127: Illustration of a churches associated z-value in latent space after being reduced to x-y coordinates. Diagram by author.
To begin this process, each original church needed to be individually transformed into its 128 latent-vector representa tion, or more commonly referred to as its “z-value”. These z-values can be obtained by encoding each 3D church with the pre-trained VAE model as described in the previous step. However, as the dimensionality of these z-values is too high to visualize (128 dimensions), they must be abstracted and reduced to just two dimensions. To do this, t-Distributed Stochas tic Neighbor Embedding (t-SNE) [59] was used to reduce high-dimensional values, 128 in this case, to low dimensional representations, two in this case, while preserving as much of the original data distribution as possible. This is done to increase interpretability and allow us to visualize complex, multi-dimensional data. When each of the 313 z-values are converted to just two dimensions (an x and y value) then displayed on a scatter plot, their position from one another within the plot represents their degree of morphological similarity. Thus, points that are closer together indicate church forms with increased similarity and ones that are far apart indicate church forms with less similarity. In this way, we can visually see and understand church’s morphological relationship to all other churches within the dataset. Unlike tradition al architectural diagrams, mapping and technical drawings that are often limited to describing a handful of buildings at a time, t-SNE dimensionality reduction provides an incredible amount of information and insight into data relationships by allowing us to visualize complex form patterns amongst hundreds or even tens of thousands of objects at once.
By testing different T-SNE parameter settings, various latent space representations were explored with the final one chosen due to its well partitioned data configuration. For example, by iteratively testing different T-SNE parameters and survey ing results, we determined which values increased cluster interpretability according to our understanding of church forms informed by existing research and expert knowledge. Though this process influenced our results, it enhanced our ability to identify and analyze patterns that could have otherwise been missed. A preliminary survey of churches within latent space partitions (as represented by clusters) hinted that they contained churches that shared similar forms according to existing research and our own knowledge of church styles.
For this paper, I used the following t-SNE parameters to reduce the dimensionality of the datasets latent-vectors from 128 to 2. Number of components was set to 2, while perplexity, learning rate, and number of iterations remained at the default values of 30, 200 and 1000 respectively.
Identifying both complex and subtle clusters among hundreds or thousands of individual z-value points can be a challeng ing, if not impossible task dependent on the size and complexity of the dataset. To assist with this task, I used a technique called “Density-Based Spatial Clustering of Application with Noise” (DBSCAN) [60] which searches for and color codes clusters according to point density. Search parameters can be controlled by setting DBSCAN hyper-parameters such as the “minimum number of samples” (mns) in a neighborhood for a point to be considered a “Core point” of a cluster, and “epsilon” (eps) or the maximum distance between points to be considered as being within the same cluster neighborhood. In this way, clusters can be more easily discovered, explored and identified via color coding to reveal both large clusters representing broader church form similarities and smaller clusters that may indicate more niche or narrowly shared form characteristics.
To better understand and interpret the morphological significance of and patterns within each church cluster, we evaluat ed them using three different methods. First, by cross checking each cluster of churches against existing research (Figure 131). Secondly, by analysis of and visual interpolation between cluster centroid and periphery data (Figure 132). Thirdly, by mapping each cluster to geographic space and comparing against agreed upon cultural zoning information (Figure 131).
To better understand and interpret the morphological significance of and patterns within each church cluster, we evaluat ed them using three different methods. First, by cross checking each cluster of churches against existing research (Figure 131). Secondly, by analysis of and visual interpolation between cluster centroid and periphery data (Figure 132). Thirdly, by mapping each cluster to geographic space and comparing against agreed upon cultural zoning information (Figure 131).
To evaluate the clustering results, we cross-checked the results against existing materials, such as church style diagrams from existing research, reference to the original 3d models, and domain knowledge gained from discussion with experts like Mykhailo Syrokhman. Through this crosscheck, we confirmed that particular church styles were being grouped togeth er within the clusters. For example, churches with a single tall tower suggesting the Transcarpathian school of design, or churches with three low towers suggesting the Boyko school of design (Figure 139, 140). In particular, Yaroslav Taras’s 2020 publication “Wooden Temple Architecture of the Ukrainian Carpathians” [25] was considered a significant ground truth benchmark for comparison as it, to the best of the authors knowledge, represents the most current, extensive, and detailed description of the Carpathian wooden churches, and their complex and interrelated styles and sub-styles as ex pressed through text , architectural drawings and clear diagramming in terms of their morphological features, evolutionary roots, and ethnic origins. His diagrams of primary church styles and sub-styles (figure 182) was very helpful as it helped to determine whether my model could organize and cluster the churches with the dataset in a similar way.
To evaluate the clustering results, we cross-checked the results against existing materials, such as church style diagrams from existing research, reference to the original 3d models, and domain knowledge gained from discussion with experts like Mykhailo Syrokhman. Through this crosscheck, we confirmed that particular church styles were being grouped togeth er within the clusters. For example, churches with a single tall tower suggesting the Transcarpathian school of design, or churches with three low towers suggesting the Boyko school of design (Figure 139, 140). In particular, Yaroslav Taras’s 2020 publication “Wooden Temple Architecture of the Ukrainian Carpathians” [25] was considered a significant ground truth benchmark for comparison as it, to the best of the authors knowledge, the most current, extensive, detailed description of the Carpathian wooden churches, and their complex and interrelated styles and sub-styles as ex pressed through text , architectural drawings and clear diagramming in terms of their morphological features, evolutionary roots, and ethnic origins. His diagrams of primary church styles and sub-styles (figure 182) was very helpful as it helped to determine whether my model could organize and cluster the churches with the dataset in a similar way.
secondary cluster #14 churches within cluster
secondary cluster #14 churches within cluster
Y. Taras church form type diagram
Figure 131: Daigram of church form pattern analysis. Diagram by author.
Figure 131: Daigram of church form pattern analysis. Diagram by author.
Lemko
Lemko
Y. Taras church form type diagram form comparison
form comparison
Northwest Early (classic)
Northwest Late South (slovak) Northeast (no tower) North (with towers) Northeast (low towers) South East (Snynskyi region)
Northwest Early (classic) Northwest Late South (slovak) Northeast (no tower) North (with towers) Northeast (low towers) South East (Snynskyi region)
South (Slovak) North
North
For the second method, we mapped each point in the cluster to geographic-space and compared their distribution to ex isting research that defined the architectural-ethnic boundaries of the various styles and sub-styles of these churches. In this way, we investigated which regional style(s) or sub-style(s) the cluster might represent by visualizing the geographic position of all churches within that cluster (Figure 131). However, as architectural-ethnic boundaries overlap, and unique church styles from one cultural region can be found deep within another, resultant observations using this final method should be considered suggestive rather than prescriptive. Yaroslav Taras’s 2020 publication “Wooden Temple Architec ture of the Ukrainian Carpathians” [25] was used as a ground truth benchmark for geographic comparison and reflection. Here, Taras paints a clear picture of the distribution and regional boundaries of the numerable styles of wooden churches by defining clear geographic architectural style boundaries according to both primary style-groupings (Lemko, Hutsul, Boyko, Transcarpathian, etc.) and secondary sub-style groups via detailed maps and diagrams. The following diagrams from his book were the most helpful. First, his cultural architecture regions map (Figure 182), which defined geographic boundaries for the various schools of folk architecture construction. Here, boundaries were decided upon by not only the latest agreed upon cultural regions, but also through the acknowl-edgement of various historic, political, religious, geomorphological, climatic, and transportation-related factors that may have affected the distribution of the various ar chitectural styles throughout the region [25]. Secondly, his architectural sub-style maps (figure 182) which suggest the geographic boundaries of the various sub-styles of the four primary schools of church architec-ture.
cluster points mapped to their geographic locations
Y. Taras’s Lemko architectural style region diagram
geographic distribution comparison
The third method was learned from, and developed by Rhee [29], assists with this by sampling the nearest church point to the centroid of the cluster. As clusters are identified by t-SNE and propagated outwards from a central dense core cluster, it can be understood that the centroid represents the “typical” or “average” church form of that cluster. This provided me with the means to retrieve the “typical form” of each cluster, thus identifying the general morphology represented within the given cluster. Equivalently, defining the furthest church from the centroid and interpolating (morphing) between the two in 3D model space, via an interpolation animation video, helps to visually see the range of morphological variation within the cluster. A custom algorithm was built to determine both the centroid church and furthest outlier church.
primary cluster #2 centroid & outlier
outlier (furthest)
centroid
= center of cluster
Figure 132: Illustration of form pattern analysis according to centroid to outlier interpolation. Diagram by author.
centroid
centoid to outlier form interpolation
With the help of DBSCAN, I was then able to objectively establish the size and range of the largest primary clusters and color code them into 7 primary groupings as shown in Figure 133, labeled Group 0, 1,2,3,4,5, and 6. See Section 5.5 Data Clustering for DBSCAN parameters used to identify clusters. By using the three methods of cluster analysis, I was then able to determine the general morphological characteristics and general church styles represented within each cluster.
7 clusters | eps: 1.936 | mns: 5
Figure 133: Latent space grouped according to loose clusters. Diagram by author.
Cluster 0 contained 70 churches that were spread out with moderate density (Figure 135). By referring to images and 3D models of each church within cluster 0 as well as existing research material, it quickly became apparent that all churches shared somewhat similar morphological characteristics. The most noticeably dominant architectural characteristic was a single tower that emerges from the roof of the narthex, or entry space of the church. While 22.9% of these towers could be considered tall, the vast majority (77.1%) of them were short. In addition, 32.8% of churches had two or three towers in total, while 67.2% did not suggesting that this cluster possibly contained atleast two primary style groups. As a note, single tower churches sometimes had a small onion-shaped dome at the opposite end of the roofline, however, these domes were not considered “towers”. Secondly, nearly all the churches were organized around a linear tripartite plan, with no or very minimal side projecting spaces extending outwards from the nave space. While some styles contain large extensions, cre ating cruciform like floor plans, such as the Hutsul School, the churches within this cluster did not. Most of these churches incorporated a single or double stepped hip roof that extended backwards from the tower over the nave and then again over the smaller sanctuary space at the opposite end. Churches with and without single or double hip roofs make up 67.2% and 32.8% of the cluster respectively.
By finding the centroid of this cluster and viewing the closest church point to it, represented as the clusters “typical form” [64], our subjective observations were confirmed as this church contained all the morphological characteristics pre viously mentioned. Interestingly, the furthest church point from the centroid appeared to be very morphologically similar to the central point. For example, having a single short tower over the narthex, a single hip roof from front to back, and a relatively small size with likely partite plan. However, after interpolating between the two churches in three dimensions, it was noticed that the further one had a much higher roofline, and a tower that did not stop at the roof above the nave but continued downwards and appeared to be nearly disconnected from the rest of the church. This interesting attribute suggested that there perhaps were multiple variations of this morphological type that needed to be explored.
Finally, by plotting the churches back to their geographic position (Figure 136) which we call “map space”, I was able to observe which architectural-cultural zones they fell into, as defined by Y. Taras style boundary diagrams, thus sug gesting their possible primary style. Interestingly, though many of the churches appear to be morphologically similar, thus suggesting a single style, the churches in fact are clustered generally within two very different primary cultural regions, Transcarpathia and the Lemko region thus suggesting that the majority of churches within this cluster are of those two cor responding schools of design. In addition, there is another cluster located within the overlapping area between these two zones. Closer inspection of churches within this overlapping area reveals a transitionary zone, where Lemko style churches begin to look like Transcarpathian churches and vice versa, thus making it difficult to tell them apart by morphological characteristics alone. This is apparent in the centroid church which contains features of both schools. The typical form as indicated by the green dot is in this zone and is of Lemko style of the East (Snynskyi region) sub-type [25]. This also suggests that further clustering must be done if we hope to determine the various church style sub-groups, or other pre viously unknown / undescribed sub-group morphological relationships or patterns within this primary cluster. Churches with similar morphology exist in the Transylvania and Bessarabia region as well [25], indicating that morphology can be shared and blended across multiple cultural regions.
= center of cluster = centroid = outlier cluster point samples
= centroid = outlier
Figure 135: Location of primary cluster in latent space (top). Diagram by author.
Figure 136: Location of primary cluster in map space (bottom). Diagram by author.
Cluster 1 contained 65 churches that were densely located within a single area (Figure 137). Like Group one, a single tower that emerges from the roof of the narthex, or entry space of the church was the most noticeable morphological trait. This was the case for 77.7% of the churches located in this group. Like cluster 0, this group also had several churches that contained 1-2 additional towers making up 25.4% of the dataset, again suggesting that this cluster possibly contained at least two primary style groups. As in cluster 1, this group also showcase a narrow linear tripartite plan without significant side projections from the nave. Similarly, the dominant roof type is a single or double stepped hip roof which was present in 73% of the churches.
The typical form centroid of this cluster revealed that this cluster represents a similar style of church as cluster one, however with a much larger and taller tower, often with a dramatically tall, pointed roof above the narthex. In addition, the single or double roof lines are higher than cluster 1 as are the exterior side walls from the ground to the bottom side of the roof line. In general, the roof also appears to be at a steeper angle, as does the roof which caps the tower and rises much higher in elevation. Overall, the form is tall and narrow, showcasing the hallmark indicators of a linear tripartite plan which is often slightly wider in the middle, and slighter shorter and narrower at the rear where the sanctuary is located [25]. By finding the furthest point, we can see that the tower is much narrower and is set slightly back from the front. The roof height is also lower and is in the form of a wider single roof that covers both the nave and sanctuary and sides of the narthex. The roof is also at a shallower angle and less dramatic than the typical form. This suggests the inclusion of alter nate, potentially outlier style groups.
Finally, by plotting the churches back to their geographic position (Figure 138), we can observe that the churches fall into three general zones. In the Northern section, they fall into the Lemko region, as some churches within the cluster contain the hallmark morphological characteristics of that style (three towers plus pyramidal roof shapes below). Though they are clustered with the other churches, they are quite unique and less related to the rest. The second group is clustered in Transcarpathia and within an area of overlap with the Boyko region. However, these churches represent the hallmark signs of the Transcarpathian school, being a single tall tower, single or double hipped roof, and a linear tripartite plan. Finally, the southern cluster represents churches within the Transcarpathia Marmures area, which are quite similar to the middle group but contain much taller pointed towers in the Gothic style of that region.
= center of cluster = centroid = outlier cluster point samples
= centroid = outlier
Figure 137: Location of primary cluster in latent space (top). Diagram by author.
Figure 138: Location of primary cluster in map space (bottom). Diagram by author.
Cluster 2 contained 63 churches that were with medium densely located within a single area within latent space (Figure 139). The most noticeable architecture element shared by 61.5% of the churches within this group is the presence of 3 pyramidal or sometimes dome towers with the middle one the highest, strongly indicative of the Boyko school of folk architecture. Of the remaining 38.5% of churches, 27.7% have a dominant central dome in the middle of the church with the remaining 10.8% having none or some other configuration indicating that perhaps the cluster is too large. Beyond the presence of a central pyramidal or dome tower, what ties all churches together is the presence of the narrow, linear tri partite plan and generally tall, narrow and long configuration of the 3 main volumes capped with one or three pyramids’ roofs or domes.
The typical form centroid of this cluster reveals a classic-style Boyko church with three tall towers surrounded by multiple tiers of decreasing sized zaloms, or rings of narrow roof overhangs that continue up the height of the tower. Though not indicative in the 3D model, which is abstracted, one can easily see the three towers and the generally long and narrow plan that makes up the tripartite configuration. The central tower is also noticeably taller than the other two, thus indicating the typical Boyko church configuration. With the furthest or “least-typical” point, we can see that only a small central tower remains flanked by two hip roofs of equal height. However, the lower volume of the church equally indicates the same tripartite plan with almost the exact same proportions as the typical form, thus indicating that the proportion of plan may be more influential on the formal characteristics of this cluster than previously considered.
Finally, by plotting the churches back to their geographic position (Figure 140), we can observe that the churches are in highest concentrations within the Boyko region as well as neighboring regions such as the Przemsyl, Lemko, and Pre-Carpathian Podnistrovska regions. There are also noticeable numbers of churches located in other, more distant regions such as within Volhynia, Opilska, Podilia, Pokut and Hutsul zones, indicating that churches with similar Boyko-like mor phology can be found in other regions as well. As Boyko churches do not always have three towers, and often just one, it becomes challenging to differentiate other single central tower churches with tripartite plans from other cultural regions such as Opilska or Volhynia. In addition, similar churches in these other regions can also have three-tower churches, there fore further complicating classification beyond pure morphology.
= center of cluster = centroid = outlier cluster point samples
= centroid = outlier
Figure 139: Location of primary cluster in latent space (top). Diagram by author.
Figure 140: Location of primary cluster in map space (bottom). Diagram by author.
This cluster contains 39 churches that are located with medium density just below cluster 2 (Figure 141), The churches within this cluster share many morphological similarities as the previous cluster which contained mostly Boyko style churches. However, what can immediately be noticed is the vertically stretched volumes of the tripartite plan before they transition into towers. This feature seems to tie these churches together, in addition to the presence of a dominant central tower (48.7% of churches) which in some churches flanked by two slightly shorter towers in Boyko-like style (51.3%). This nearly 50/50 split separates the cluster into two distinct groups, however, similar to the previous group they all share the same tripartite plan, though 23% of churches show noticeable widening of the central nave space, thus indicating possible influence from the Hutsul School which is the only primary style which has nave extensions and cruciform-like plans.
The typical form of this cluster appears quite like the typical form of cluster 2, however, in a side-by-side compar ison, it shows significant vertical stretching of the main church volume below the start of the towers. This vertical empha sis reaches its maximum in the least-similar form which has pronounced vertical elongation capped by three domes that narrow and terminate at a sharper point. Additionally, the widening of the central volume which holds the tallest dome can easily be seen, thus suggesting that the morphometric characteristics of cruciform plans are being included within this cluster
By plotting the churches back to their geographic position (Figure 142), we can see that the churches within the cluster primarily fall within two cultural regions, being the Boyko and Opilska regions, however, are also overlapping in the neighboring region of Przemsyl. This indicates the presence of similar morphological characteristics within these three zones. Another smaller cluster is in the Pokut zone, which, to the best of the authors knowledge, can have church styles characterized by slightly cruciform plan and vertically stretched volume and a small cluster in Podilia. We can also see how the least-typical church form is located far outside of these groups, indicating its outlier state given its lack of geograph ic neighbors. However, its form does begin to come close to a number of churches located in the Pokut zone. Given this mixture, Cluster 3 clearly contains numerous styles that although are the result of the unique communities within these different cultural regions, do share several interesting overlapping morphological traits.
= center of cluster = centroid = outlier cluster point samples
= centroid = outlier
Figure 141: Location of primary cluster in latent space (top). Diagram by author.
Figure 142: Location of primary cluster in map space (bottom). Diagram by author.
This cluster contains 22 churches that are located with medium density to the right of cluster 3 (Figure 143). The churches within this cluster seem to fall into two general categories. First, 45.4% of them that have a dominant cruciform plan or ganization while 54.6% have are organized volumetrically according to a linear tripartite plan. Those with cruciform plans typically have a single central tower (80%) with the remaining 20% having three towers with the middle tower being the tallest. Those with a linear tripartite plan are split into two primary groups, with the first (41.6%) having a single central tower, and the remaining ones having three pyramidal or dome like towers with the central one being the tallest. Thus, tower count was not the primary trait of this group. By comparing churches within this group to existing studies and do main knowledge, this cluster was determined to contain the morphological characteristics that suggest that both the Hutsul school, which here is represented with a cruciform plan with one or three central towers, and Boyko style, which here is represented by the churches with linear tripartite plans with one central or three towers. However, some churches which appear to be Boyko suggest the Opilska school which are in some cases can be morphologically similar.
This mixture of Schools can be seen more clearly when plotting the churches back to their geographic position (Figure 144) as they tend to fall into three primary cultural regions. First, the Hutsul zone which is characterized by cru ciform plan including the neighboring Pokut school can include similar morphological features, the Boyko School and the Opilska School. This cluster is perhaps too broad and accounts for too many morphological categories (cruciform plans and tripartite plans), suggesting that further secondary clustering should be done
Interestingly, interpolating between the typical and least-typical form of this cluster does not showcase these two groups (5-11). Rather, it shows two churches that both have linear tripartite plans, with the least-typical form being a very extended and long version of the typical form. If we refer to latent space, we can see how the typical form is taken from be tween two smaller vertically stacked clusters, and the least-typical form is taken from the top cluster. From this observation and through other analytical means, we can deduce that the top small cluster represents churches with linear cruciform clusters incorporating the hallmark Boyko and Opilska architectural characteristics, and the lower one tends to represent churches with Cruciform organizations indicative of them belonging to the Hutsul or Pokut style. By interpolating between the typical form of these two groups, we can see that in fact, they both share the similar formal qualities of a central large tower with two long hip roofs extending from either end with somewhat similar proportions. However, the lower cluster has two additional perpendicular roofs extending from the central tower to satisfy the cruciform configuration pattern. This may explain both their proximity and separation from one another.
= center of cluster = centroid = outlier cluster point samples
= centroid = outlier
Figure 143: Location of primary cluster in latent space (top). Diagram by author.
Figure 144: Location of primary cluster in map space (bottom). Diagram by author.
This small cluster contains just 11 churches that are located with high density nearby and to the right of cluster 4 (Figure 145) and includes both cruciform and linear tripartite morphology traits with one central or two or three towers. It is also in medium proximity to cluster 1 which represents single tall tower morphological traits. As a result, cluster 5 seems to con tain churches with characteristics of both clusters including both cruciform layout churches (54.5%), tripartite plans with one central or two or three towers (18.2%) similar to cluster 4, and churches with a single medium to tall tower over the narthex (27.3%) in the style of cluster 1. The low number of churches within this cluster and geographic dispersal suggest that perhaps all of them are morphological outliers. For example, one of the churches in this group has features of both the Hutsul-dominant cruciform plan, while simultaneously showing the typical three tall tower forms ribbed with zaloms of the classic Boyko style (second church photograph from left in below figure). Thus, perhaps explaining the churches posi tion in latent near areas that represent both the cruciform style and three tall tower style suggestive of the Boyko School. Referring to existing studies and research regarding these churches, it becomes apparent that this small cluster does not represent a single dominant morphological characteristic that describes the entire cluster, but rather an interesting mixture of geographically scattered churches from the dataset which contain less represented smaller sub-style forms, of which half contain cruciform plans.
Interpolating between the typical and least-typical form of this cluster reveals this diversity as the morphologi cal differences between a cruciform church with three towers is quite different than a linear-tripartite-plan church with a single tall tower over its narthex. This suggests that this cluster, though dominated by churches with cruciform plan suggesting a strong presence of Hutsul-style churches, should not be assumed to have a single dominant morphological characteristic, but rather, a mixture representing different morphological styles.
This is further reinforced by the dispersed nature of the cluster in geographic space. Again, there is a higher con centration around Hutsul cultural region, but again churches are still dispersed, suggesting that they may reflect a variety of sub-styles, which could be misinterpreted if referring to the latent space diagram alone.
= center of cluster = centroid = outlier cluster point samples
= centroid = outlier
Figure 145: Location of primary cluster in latent space (top). Diagram by author.
Figure 146: Location of primary cluster in map space (bottom). Diagram by author.
The final cluster contains 16 churches that are located with medium density directly beside cluster 0 and cluster 1 and is characterized by the presence of three narrow towers (62.5% of churches within the cluster) located along a long and narrow volume characterized by a slightly wider middle section likely indicating a tripartite plan (81.3%). For churches with 3 towers, 80% of them reduce in height from front to back with the first tower being the largest and most prominent. The roof structure below the second and lowest tower in the rear are also broken up and step downward in height. This feature is hallmark of the classic Lemko style. The other 20% have towers that are of the same height, with 66.6% of them having a single hip roof of consistent height. By referring to existing form studies, this also suggests the Northeast sub-style of the Lemko School of design [25]. Also present is a group of churches with very long and narrow volumes containing a single hip roof broken by a single low projecting tower. These geographically concentrated churches, though similar in morphology do not appear to satisfy any of the defined style-guidelines laid out in previous research and may be variations or outliers within their class.
Identifying the typical form centroid of this cluster helps me establish the dominant presence of churches with three-tower configuration that gradually lowers in height from front to back, sitting upon a long rectangular volume, thus indicating a linear tripartite plan. The least typical form located furthest from the centroid was not well encoded, thus does not adequately represent the original structure. The encoded form looks more like the centroid form, which perhaps explains why it can be found within close proximity in latent space.
= center of cluster = centroid = outlier cluster point samples
= centroid = outlier
Figure 147: Location of primary cluster in latent space (top). Diagram by author.
Figure 148: Location of primary cluster in map space (bottom). Diagram by author.
By referring existing research [24, 25, 35, 36, 38, 39, 40], I was able to assign ground truth (G.T.) style labels to each church point in our 2D latent space scatter plot (Figure 149). These labels indicate which School of folk temple construc tion they actually belong to in reality according to existing research. This includes the primary four schools including; Boyko (1), Hutsul (2), Lemko (3), and Transcarpathian (4), in addition to other schools such as Pokut (5), Bukovina (6), Pre-Carpathian Podnistrovska (7), Opilska (8), Przemsyl (9), Kholmshchyna (10), Podilia (11), Volyn (12), Transylvanian (13), Bessarabian (14), and Southern Malopolska Gothic (15), all of which are represented in lesser volumes within the dataset. Part of the Transcarpathian School labelled as the Marmures (16) style given the unique style of that area and overlapping style names, “Transcarpathian style” or “Marmures style”, used by various researchers [25, 61]. By having these labels, I was able to better evaluate how well our model was able to cluster capture the primary morphological traits of these schools of folk architecture construction. However, it is important to note that church morphology does not always
indicate what architectural cultural school it belongs to due to the morphological overlap that exists between these prima ry styles [25]. Thus, it is expected that churches with the same label may appear in multiple clusters due to these ranges in form within primary style groups. For example, the morphology of the Lemko East Snynskyi region sub-style does not look like a “typical” Lemko church but looks startlingly like the Transcarpathian inter-mountain region sub-style and vice versa [25]. Such overlaps previously and newly discovered in this work will be discussed in the remainder of this chapter.
When referring to the latent space diagrams, it is clear that there is correlation between the G.T. labels and the original clusters predicted by the model. As each predicted cluster represents a dominant and primary morphological char acteristic, for example, churches with only one tall tower above the narthex, we can see how two or more dominant traits can fall within the latent space specified as a single primary style by the G.T. labels.
Boyko Hutsul Lemko
Transcarpathian Pokut Bukovyna
Pre-Carpathian Podnistrovska Opilska Marmures Podilia Volyn
Transylvania Bessarabia South Malapolska Gothic Unkown
Figure 150: Location of ground truth labels in map space. Diagram by author.
The Boyko G.T. style labels appear to overlap both cluster 2 and 3 predicted by our model (Figure 151). Again, the ground truth style label indicates which points / churches in the latent space are actually of a particular style, in this case Boyko style. This can also be projected back to geographic space to see the real distribution of Boyko churches. Given this, the following diagrams, suggest that Boyko churches may have two primary morphological forms as two large clusters fall within the same general area in latent space as the ground truth Boyko labels. The first form includes churches with ver tically shorter or more squat tripartite volumes below the towers as discovered when analyzing the typical morphology cluster 2, and the seconmd form churches with vertically taller tripartite volumes below the towers as learned from cluster 3. When comparing the geographic space diagrams, we can see that group 2 and 3 contain other churches that share sim ilar morphological traits that are not labelled as “Boyko” by the G.T. labels. When taking a closer look at the data as well as the geographic space, it becomes clear that churches within the Opilska, Przemsyl Pokut and Podilia, all regions that contain churches which share similar morphological traits to Boyko-style churches, thus suggesting further sub-clustering and inquiry to uncover additional sub-style clusters.
g.t. Boyko style label latent space distribution
cluster latent space distributions
2 3 3 2
g.t. Boyko Church geographic distribution
cluster geographic distribution
Figure 151: Church points with ground truth labels in latent and map space (left column) compared to primary clusters that best correlate with their latent space position (right column). Diagram by author.
The Hutsul G.T. labels appear to loosely collect around the bottom perimeter of the latent space distribution and does not seem to be represented strongly by any single predicted cluster (Figure 152). To begin, there are far less Hutsul churches represented in the dataset, so perhaps the model did not have enough volume to properly learn and cluster the style ac cordingly. Nevertheless, the position of this style according to the G.T. labels is robustly designated around the bottom at the periphery of the entire distribution.
The two clusters that best represent the Hutsul morphological style and overlap the G.T. labels include clusters 4 and 5. Cluster 4 contains morphological elements that represent the classic Hutsul form, including a cruciform plan and geometric organization and a single or triple tower configuration with the central tower being the tallest. Cluster 5 rep resents morphological traits that are undoubtedly cruciform in plan, but representative of schools that are not necessarily located or related to the architectural-cultural Hutsul zone. It also contains churches that contain both Hutsul and Boyko elements simultaneously, indicating an interesting stylistic mixture of two major schools of folk temple construction.
By referring to the geographic space diagrams, we can see that churches with cruciform plans and tall central towers exist in zones like Lemko, Przemsyl, Podilia, Pokut, Boyko, etc. Y. Taras notes that the Hutsul school has spread much further than the others, which may explain why it is so prevalent within other regions [25]. Further combinations of sub-clustering, geographic space mapping, and references to existing research may reveal other interesting sub-styles and morphometric correlations between these interesting cruciform style churches.
g.t. Hutsul style label latent space distribution
cluster latent space distributions
4 5 4 5
g.t. Hutsul Church geographic distribution
cluster geographic distribution
Figure 152: Church points with ground truth labels in latent and map space (left column) compared to primary clusters that best correlate with their latent space position (right column). Diagram by author.
The Lemko G.T. labels appear to overlap cluster 0 and 6 predicted by our model (Figure 153). The first and larger cluster is more concentrated in the middle, while the second cluster which is much looser is located just above and to the left. This suggests that there are at minimum, two different primary morphological forms represented within Lemko-style churches.
First, Lemko G.T. labelled churches that fall within predicted cluster 0 may contain dominant towers over the narthex and a tripartite plan, while the ones that fall within cluster 6 are represented by the classic Lemko morphology but may also include a number of Lemko sub-styles such as the North-east and Northwest sub-style.
By referring to the geographic space diagrams, we can see that cluster 0 covers a broad range including the Lemko zone, thus suggesting that this cluster needs to be broken down into sub-clusters to obtain a more detailed understanding of the morphological sub-styles within this group. Due to our domain knowledge, we know for a fact that Transcarpathian and Lemko churches have distinct morphological characteristics and will likely fall into separate predicted clusters if fur ther investigation is taken. Given this, we also learn that these two styles are related and may likely overlap in form given their close proximity to each other in latent space.
g.t. Lemko style label latent space distribution
cluster latent space distributions
0 6 0 2 6
g.t. Lemko Church geographic distribution
cluster geographic distribution
Figure 153: Church points with ground truth labels in latent and map space (left column) compared to primary clusters that best correlate with their latent space position (right column). Diagram by author.
Take, for example, the area designated by the “Transcarpathia” G.T. labels which encompass the area of two clusters pre dicted by our model (Figure 154), cluster 0 and cluster 1. From this, we can presume that Transcarpathian churches have two primary morphological forms. The first, being churches with a single short tower over the narthex with a tri-partite plan and hip roof as designated by cluster 0, and the second being churches with a single very tall tower with a tripartite plan and a typically steeper roof and higher exterior side walls as designated by cluster 1. However, in geographic space it becomes apparent that churches with similar morphological characteristics as the Transcarpathian school exist in other schools of folk temple construction. For example, within the Lemko architectural-cultural zone, which as we have learned contains many churches with tall towers over the narthex as well as linear tripartite plans. This suggests that the morphol ogy of churches from the Lemko and Transcarpathia zones are related.
g.t. Transcarpathian style label latent space distribution
cluster latent space distributions
2 6
0 6 0
g.t. Transcarpathian Church geographic distribution
cluster geographic distribution
Figure 154: Church points with ground truth labels in latent and map space (left column) compared to primary clusters that best correlate with their latent space position (right column). Diagram by author.
As primary clusters only provided a general understanding of the primary morphological style groups represented within our dataset, I re-clustered our latent space distribution in order to reveal more nuanced form similarities and patterns that were not yet identified. See Section 5.5 Data Clustering for DBSCAN parameters used to identify these secondary clusters. From initial observations we can see that many of our primary clusters could be broken into three to four smaller clusters.
6 clusters | eps: 1.936 | mns: 5
Figure 155: Latent space grouped according to medium sized clusters. Diagram by author.
(Figure 155). After re-clustering, I identified fourteen sub-clusters that could potentially help describe more specific substyles. Due to the number of clusters identified, the remainder of the section will only briefly describe the sub-clusters identified within primary cluster 0.
Figure 156: Location of secondary clusters in map space. Diagram by author.
Primary cluster 0 was partitioned into four secondary clusters named sub-cluster 0 (purple) , 2 (dark blue), 11 (orange) and 14 (red) by re-clustering the data into smaller groups (Figure 157). To recall, primary cluster 0 contained smaller volume churches with linear tripartite plans with a single short tower above the narthex and minimal side projections from the nave. Initial observations suggests that secondary clusters can better define particular regional sub-styles, evidenced by the reduced number of geographic outliers present when mapping clusters back to geographic space.
6 clusters | eps: 1.936 | mns: 5
Figure 157: Primary cluster 1 partitioned into smaller medium sized clusters. Diagram by author.
The final cluster contains 16 churches that are located with medium density directly beside cluster 0 and cluster 1 and is characterized by the presence of three narrow towers (62.5% of churches within the cluster) located along a long and narrow volume characterized by a slightly wider middle section likely indicating a tripartite plan (81.3%). For churches with 3 towers, 80% of them reduce in height from front to back with the first tower being the largest and most prominent. The roof structure below the second and lowest tower in the rear are also broken up and step downward in height. This feature is hallmark of the classic Lemko style. The other 20% have towers that are of the same height, with 66.6% of them having a single hip roof of consistent height. By referring to existing form studies, this also suggests the Northeast sub-style of the Lemko School of design [25]. Also present is a group of churches with very long and narrow volumes containing a single hip roof broken by a single low projecting tower. These geographically concentrated churches, though similar in morphology do not appear to satisfy any of the defined style-guidelines laid out in previous research and may be variations or outliers within their class.
Identifying the typical form centroid of this cluster helps me establish the dominant presence of churches with three-tower configuration that gradually lowers in height from front to back, sitting upon a long rectangular volume, thus indicating a linear tripartite plan. The least typical form located furthest from the centroid was not well encoded, thus does not adequately represent the original structure. The encoded form looks more like the centroid form, which perhaps explains why it can be found within close proximity in latent space.
= center of cluster = centroid = outlier cluster point samples
= centroid = outlier
Figure 159: Location of secondary sub-cluster in latent space (top). Diagram by author.
Figure 160: Location of secondary sub-cluster in map space (bottom). Diagram by author.
Sub-cluster 2 is located at the bottom of primary cluster 0 and also contains 17 churches which are also reflective of the typical morphological characteristics of its parent cluster (Figure 161). The majority of churches share nearly identical formal characteristics and are difficult to tell apart if referring to morphology alone. The centroid church represented in the cluster represents the typical form of the cluster and contains a single hip roof configuration and stubby short tower. The least similar church is quite different and represents the Slovakian Lemko sub-style [25], characteristic of significantly shorter towers, a lower and a more vertically compressed tripartite volume, often the lack of a rear tower, and an overall more simplified, spartan geometric form. Interpolating between the two reveals this. It may be noticed however, that the differences between this sub-style of Lemko church and the typical form is not as drastic as it would have been expected and only includes an additional tower in the center and a lower roof over the nave. If comparing the typical form of this group to a classic Lemko church with three massive towers, the morphological difference may be different. This may ex plain why this particular sub-style church is located within this cluster. When plotting back to geographic space (Figure 162), we can see that these churches fall within the Northern area of the Transcarpathian zone and the Southern part of the Lemko Zone, again suggesting a location of potential architectural-cultural overlap and influence between styles.
= center of cluster = centroid = outlier cluster point samples
centroid outlier
Figure 161: Location of secondary sub-cluster in latent space (top). Diagram by author.
Figure 162: Location of secondary sub-cluster in map space (bottom). Diagram by author.
Sub-cluster 11 is located at upper left corner of primary cluster 0 and also contains 18 churches which are somewhat dif ferent than the typical morphological characteristics of its parent cluster (Figure 163). Within this group, the front tower of these churches seems to be pulled away from the body of the church and sits proud to the main volume of the building. This can be noted by the tower walls continuing to the ground in almost all examples shown, compared to towers that terminate at the roof, in a way that is typical of the majority of churches within parent cluster 0. This indicates that these churches are likely from another architectural-cultural zone.
However, the cluster also contains churches that appear different from one another, with some looking like those in sub-cluster 2. After finding the typical form we can see that indeed, the average morphology of this cluster includes a tower that sits proud to the main body of the church. The least-typical example also shares this configuration, but with the tower completely separated from the rest of the church except for a low connecting hall at ground level. After plotting to geographic space (Figure 164), we can see how churches within this cluster are located within 3 different zones, Przemsyl, Lemko and Boyko regions. However, most are located within the North ends of these groups in Poland, which may indicate a unique style of church within that area which has not been studied in this work.
= center of cluster = centroid = outlier cluster point samples
centroid outlier
Figure 163: Location of secondary sub-cluster in latent space (top). Diagram by author.
Figure 164: Location of secondary sub-cluster in map space (bottom). Diagram by author.
Sub-cluster 14 is located at the bottom of primary cluster 0 and also contains 13 churches which showcase the classic three stepped tower configuration with linear tripartite plan configuration of the Classic lemko style ( 165). After reference to existing research [25], it was discovered that this sub-cluster seemed to contain morphological elements characteristics of multiple Lemko sub-styles including the Late and North (low towers) sub-styles.
After plotting this cluster to geographic space (Figure 166), we can see that indeed, these churches all come from the Lemko zone and seem to be locate in the North central and Northeast areas, which is where these sub-styles are locat ed. Though I found the typical form of this group, it did not seem to be well encoded as I had hoped and thus was only a low-resolution representation of the ground truth building volume. We believe this may be because these particular substyles of Lemko churches have much lower towers than their tall counterparts and may have been more difficult to encode due to their similarity with other styles such as the Transcarpathian style. As we can see, the least-similar form is a Tran scarpathian style church which other than the lack of middle and rear small towers like the other churches in this group, is in fact quite similar, that is, having a single tall tower over the narthex, and a double height roof dropping in height from the nave to the sanctuary. The interpolation between the two churches, though at low resolution, shows that other than
= center of cluster = centroid = outlier cluster point samples
centroid outlier
Figure 165: Location of secondary sub-cluster in latent space (top). Diagram by author.
Figure 166: Location of secondary sub-cluster in map space (bottom). Diagram by author.
Tertiary clustering revealed a whole host of small clusters which suggested a number of more nuanced sub-styles within the larger secondary clusters. See Section 5.5 Data Clustering for DBSCAN parameters used to identify these secondary clusters. There were 49 sub-clusters identified that ranged in size from 4 to up to 20 churches. This high prevalence of
Figure 167: Latent space grouped according to small sized clusters. Diagram by author.
tertiary clusters makes a great deal of sense given the range of existing church sub-styles and even small micro-styles which are noted to also exist but have not yet been rigorously categorized compared to their parent sub-styles. For this chapter, the tertiary clusters from sub-cluster 14 will be described.
Figure 168: Location of tertiary micro clusters in map space. Diagram by author.
Primary cluster 0 was partitioned into four secondary clusters named sub-cluster 0 (purple) , 2 (dark blue), 11 (orange) and 14 (red) by re-clustering the data into smaller groups (Figure 169). To recall, primary cluster 0 contained smaller volume churches with linear tripartite plans with a single short tower above the narthex and minimal side projections from the nave. Initial observations suggests that secondary clusters can better define particular regional sub-styles, evidenced by the reduced number of geographic outliers present when mapping clusters back to geographic space.
6 clusters | eps: 1.936 | mns: 5
Figure 169: Location of Tertiary micro-clusters within secondary sub-cluster 14 Diagram by author.
* For analysis of tertiary clusters, please refer to Chapter 5: “Findings” and the “Micro Patter 1: Lemko Church Form Transion” sub-section.
Figure 170: Location of these tertiary micro-clusters within map space. Diagram by author.
By replacing each point in latent space with its corresponding 3D church model, we can more easily see and interpret the morphological similarities and differences amoung the various styles and sub-styles of Carpathian wooden churches within the dataset. Again, churches closer together indicate a higher degree of formal similarity, while churches further away from one another indicate a lower degree of formal similarity. The clearest example of this is the dense cluster of mostly Transcarpathian style churches (1) at the far right side of the distribution that have a single, very tall and slender gothic-style tower over the Narthex. The group of churches immediately to the left (2) also shares this feature, but includes two smaller towers behind the main one and a different roof configuration. Upon closer inspection, we can see that this second group is made up of Lemko style churches. Though a completely different style, the shared form of having the most prominent tower over the narthex (front entry space of the church) has placed them in closer proximity to the first
group. In addition, the Lemko group is placed to the right of a cluster of Boyko-style churches (3) as they also share over lapping morphology. Here, both Lemko and Boyko churches incorporate a 3 tower configuration, tripartite plan and other subtle similarities. However, Boyko churches differ dramatically as they have significantly shorter pyramidal / domed tower with the tallest over the nave in the middle, as well as other unique features. As a result, the Lemko church is placed between both Transcarpathian and Boyko churches as they incorporatess elements of both styles. Finally, it is apparent that various churches seem to be “out of place”. For example, a church with no towers surrounded by a churches with three distinct towers. This may be due to 3D reconstruction distortions, insufficient examples of that style to properly encode and place them within a distinct group of its own, or less apparent morphometric similarites that are difficult to identify. However, churches in between large clusters may represent hybrid sub-styles that incroporate elements of both.
By observing relationships and repeating trends between clusters, sub-clusters, and tertiary cluster through a combination of typical and least typical form finding, reference to existing research and expert domain knowledge, easy dataset acces sibility, and geographic space observations, larger morphological-based patterns could be found discovered and tracked within the overall latent space data distribution. These groupings of similar data within latent space are also referred to as “manifolds”. In this way, I could decipher both macro and micro-organizations of the latent space by observing relation ships between its various manifolds, thus providing novel insight into the unique morphological patterns hidden amoung the hundreds of individual Carpathian wooden churches.
The first macro pattern identified partitions the entire latent space distribution in half diagonally from the extreme top left to the extreme bottom right. The right half, includes churches that almost always have its tallest (and usually only) tower located at the front of the church over the narthex. The left half, includes churches that almost always have its tallest tower over the center of the building above the nave. Though some churches on the left side of this dividing line are towerless, they almost always have a stepped hip-roof configuration with the highest hip roof in the middle over the nave, and two lower step hip roofs of equal hight on eitherside over the narthex and sanctuary. This demonstrates how all churches with in the dataset as well as their associated styles and forms can be generally organized, identified, and related to one another according to tower height and placement.
The second macro pattern identified is the general partitioning of latent space into four zones according to different ar chitectural form characteristics: Zone 1 includes churches with one tower over the narthex at the front. Zone 2 includes churches with three towers with the tallest over the Narthex at the front. Zone 3 includes churches with the tallest tower over the Nave in the middle. Zone 4 includes churches that have cruciform floor plans.
This suggests that our model was able to generally differentiate the four main schools of folk architecture that were primarily represented within the dataset: Hutsul, Lemko, Boyko and Transcarpathian. These four configurations, “single tower in front”, “triple stepped towers”, “triple / single central towers”, and “cruciform plan” are located in 4 zones as in dicated by the red dotted lines in the diagram with typical church form and zone number indicated by the large dark grey church silhouettes. The first configuration located in zone 1, has a single dominant tower at the front is located from the middle to the upper right-hand portion of the latent data distribution. This configuration is often, is typically associated with the Carpathian School but also includes Lemko churches that appear quite similar in form. The second configuration located in zone 2, has three towers that step down in height from front to back is located in a small central cluster just be low the first. This proximity is likely due to both having a tall belltower at the front above the narthex, as well as the shared linear tripartite plan. This configuration is often, but not always associated with the Lemko School. The third configuration located in zone 3 has either a single central or three towers that sit over a linear tripartite form with the central tower being the tallest. This configuration is often, but not always associated with the Boyko School. The final configuration located in zone 4 and also has a single, three or even five towers with the central tower being the tallest but incorporates a cruciform plan. This configuration is located at the bottom of the distribution, and beside the previous configuration due to their overlapping morphological features of having a central tower. This configuration is often, but not always associated with the Hutsul School.
The third macro pattern identified is the change in tower height and number of towers present. This particular macro pattern occurs in multiple directions across the latent space, indicated by the red arrows in the figure. By finding and illus trating this trend, I obtained a deeper understanding of the dataset by providing a means to compare the tower height and count of one church to the next. It is also clear that this pattern does not occur in a single linear direction, but rather in a complex multi-directional manner across the latent distribution diagram. This complexity makes it difficult to both detect and track without careful study of each cluster and an intimate knowledge of the subject through domain knowledge and experts and reference to existing research and geographic space.
As indicated, this trend has 6 main “arteries” of direction. Within zone 1, tower height drastically increases from left to right as shown by the churches with small towers in the upper left portion of the zone and the churches with ex tremely tall and narrow towers at the far right of the group. This left to right configuration continues within zone 2 where not only the front tower increases in height but also the rear two within this three-tower configuration. This trend within zone 2 is illustrated within 6. Results. 6.5 Tertiary Clusters. Within zone 3, the direction of tower height change rotates 90 degrees to a vertical orientation travelling from the middle up where tower height decreases and middle down where tower height decreases again. Decreasing tower height is also accompanied with a decrease in tower count, typically from 3 to a single central tower. Within zone four, both tower height and count increase from vertically from the upper portion of zone 4 into zone 2 which is occupied by the Lemko-style triple stepped tower configuration. Along all of these arteries, we typically find churches that more or less follow these patterns. Though this is not guaranteed, it does represent the majority of cases, especially within zone 1 and 2 where it is almost universally followed.
Figure 176 illustrates the first micro trend; how the form of Lemko churches morphs across the latent and geographic space in terms of tower height and size. The transformation occurs from left to right within a medium sized cluster near the center of the distribution, partitioning three smaller clusters based on a particular height and size of tower: the cluster in green includes churches with the shortest towers; the cluster in blue includes medium sized and height towers; the cluster in red includes the largest and tallest towers. These three clusters were then compared to Taras’s sub-style diagrams, and somewhat align with three distinct Lemko sub-styles. The shortest (green dots) are associated with the North (with towers) sub-style, the medium (blue dots) with both South (Slovak) and Northwest Late sub-styles, and the tallest (red dots) asso ciated by primarily Northwest Late sub-styles. By identifying this pattern, we can understand how both tower height and size are important architectural characteristics that help define or relate particular Lemko church sub-styles to one another.
secondary cluster #14 churches within cluster
increasing tower height
short towers medium towers tall towers
176:
Y. Taras church form type diagram
tall tower cluster
Northwest Early (classic)
Northwest Late South (slovak) Northeast (no tower) North (with towers) Northeast (low towers) South East (Snynskyi region)
centroid to outlier form interpolation
medium tower cluster
short tower cluster
sub-styles.
cluster points mapped to their geographic locations
cluster point locations overlayed ontop of Y. Taras’s Lemko church style region diagram
The below figure illustrates the morphometric relationships between the Classic Boyko sub-style and the Northwest Late Lemko sub-style churches, and how their forms morph from one to the other, as represented by a series of transitory inter mediate, or hybrid styles, across latent space. This transformation occurs along a curving path from left to right as indi cated by the red arrow below with the Classic Boyko sub-style (blue dot) churches loosely clustered around the left-middle side of the distribution and Northwest Late Lemko sub-style churches (yellow dot) tightly clustered within the middle. As you move between the two points in latent space from left to right, we encounter various church styles which appear to be represent transitionatory stages between these two distinct styles. For example, a particular Boyko Uzhansko-Lyutyansk Region sub-style church (green dot) begins to incorporate a noticeably higher tower over the narthex, a feature which is rare in Boyko churches but common in Lemko Churches. To the right, and closer to the Lemko cluster, we find another Boyko Uzhansko-Lyutyansk Region sub-style church (purple dot) which also begins to incorporate a taller tower over the
Style transition path in latent space latent point 3d churches
latent point church elevations
narthex. Furthermore, the rear tower over the sanctuary is much lower, thus beginning too appear more like a Lemko than Boyko church. Interestingly, both of these churches are located near the border between the Boyko and Lemko ethnic region, thus suggesting how their geographic position, and relationship between both regions may have influenced their unique mixed-style. Next, as we move to the right again within latent space and further into Lemko territory within geo graphic space, we encounter an additional church in Lemko South (Slovak) sub-style (red dot) that appears to be closer in style to the typical Lemko church but still maintains a number of classic Boyko style architectural features such as the multi-stepped pyramidal roofs. Overall, this pattern illustrates how our model could linearly organize this transition be tween Boyko and Lemko style within latent space, which then could be easily identified. Furthermore, when plotting these patterns to geographic space, this transition also appears to follow a linear pattern as well with the most noticeable hybrid styles occuring in the border regions between both cultural zones, suggesting how style gradually transitions over space.
Y. Taras church form type diagrams
Lemko sub-styles
Style transition path in map space
Boyko sub-styles
Northwest
Northwest Early (classic) Northwest Late South (slovak) Northeast (no tower) North (with towers) Northeast (low towers) South East (Snynskyi region)
Archaic Classic
Uzhansko-Lyutyansk Region Bolehivso-DolynskPereginsk Region Sambir-DolynskRegion
Another micro-pattern identified demonstrates the relationship between Transcarpathian Marmures regional style (red dots) and Lemko style (greend dots), and how they transition from one to the other. In latent space, both styles are found within their own clusters at the right side of the distribution, with the Marmures churches at far right and the Lemko cluster to its left. Points in between represent a mixture of various Transcarpathian sub-styles. However, their proximity to one or the other determines the their level of morphological similarity to either the Marmures or Lemko style. For example, points closer to the Lemko cluster begin to express Lemko-like features, like additional towers or lower sanctuary rooflines. Similarily, points closer to the Marmures cluster begin to reflect their signature taller towers and continues rooflines over the nave and narthex. As such, we can observe transitionary gradient of church sub-styles exist from right to left that repre sent a incremental morphing of form between the Marmures and Lemko style. In map space this is reflected geographically in a Northwest direction beginning at the Marmures region at the bottom right corner and ending in Lemko region in the
Lemko (mix of sub-styles)
Lemko
3D churches church elevations
Transcarpathian (Marmures region)
Transcarpathian (Khust-Dubiv region)
Transcarpathian (primarily Inter-Mountain region)
Marmures
Marmures
Style transition path in Latent Space 1 2 3 2 1 3
top left corner. The order of points in-between also generally correlatesto their order in latent space, suggesting how style transitions occur incrementally over geographic space as well. These in-between points were seperated into two more groups according to their geographic seperation. Blue dots represent churches in the Khust-Dubiv regional sub-style which neighbors the Marmures region but has shorter bell towers and different roof styles. In latent space, blue and red dots mix, thus suggesting high form similarity. Yellow dots, are even further away geographically and have more formal differences such as even shorter towers and a squatter and compact appearances. However, being closer to the Lemko region, some churches within this grouping incroporate Lemko-like features. For example, “yellow-dot #3” in the Kraynian-Svalyasko Ploskiv regional style showcases the clear step-like roof lines and extra boroque towers over the nave and sanctuary and is believed to infact be an early example of Lemko-style devlopment. Interestingly, it is positioned clearly between the Mar mures and Lemko grouping in latent space, thus clearly indicating its morphological relationships to both styles.
Y. Taras church form type diagrams
latent points mapped to their geographic locations
Marmures Region*
Kraynian SvalyaskoPloskiv Region
Svalyavsk Region
Kolochava Region Khust-Dubiv Region
Velikovychiv Region Velikovereznian-chor nogolovska Region
Inter Mountain Region Bohdaniv-Yasignya Region
Transcarpathian sub-styles
Northwest Early (classic)
Northwest Late South (slovak) Northeast (no tower) North (with towers) Northeast (low towers) South East (Snynskyi region)
Lemko sub-styles
Lemko 1
2 3
Marmures
* Not included in Y. Taras diagram but was added by author as Marmures style has been mentioned by several sources []
A conversation with Mykhailo Syrokhman revealed that certain church sub-styles can only be found within certain valleys or along short stretches of rivers. In order to identify these micro-style using DL-methods, the latent space distribution was re-clustered in a way that only highlighted the smallest and most tightly grouped church points. As a result, two clusters were detected: a small cluster of two Boyko churches within a single valley (Figure 179) and a group of three Transcar pathian School churches along the same stretch of river (Figure 180). Both built during the 18th century and located only four kilometers apart, these two Boyko churches appear almost identical and embody a unique Boyko sub-style possibly endemic to that specific secluded region.
The three Transcarpathian churches have two neighboring latent space tertiary-clusters. They are located in close proximity to one another along a short stretch of the Latorica river within the northern Transcarpathian zone. The churches were built around the early 19th century. They are almost identical, but do not appear to fall within a described substyle category, suggesting the presence of a unique micro-style within that region repre sented by just a few examples. Finding these groups highlights how DL-methods can assist us with identifying multiple unique micro-styles relatively easily amongst datasets of hun dreds or tens of thousands of examples.
Another noticeable form pattern indicated by the purple arrow is the transition from tripartite to cruciform plan which occurs between the bottom of zone 3 and zone 4 (Figure 181). Though the typical form of zone 3 is mainly represented by a linear-tripartite plan, it does start to transition towards a cruciform plan at the bottom as it nears zone 4 (church 1). This is marked by churches that have increasingly wider nave spaces which in turn, begin to give the church a noticeable cruciform shape (church 2). With churches nearer to zone 4, these projections tend to get larger, hence increasing this cru ciform effect. Once in zone four, the most pronounced cruciform shape is present (church 3). From these observations, we can assume that as churches within zone 3, represented as points, approach the border with zone 4, they may increasingly express a cruciform plan configuration, with the most pronounced configurations being found deep within zone 4.
An additional micro trend revealed three churches that appeared to be nearly identical though they are part of only two dif ferent primary style groups (Figure 182): Lemko Snynskyi sub-style (#1, 2) and Transcarpathian Velikobereznyansky-Chor nogolovska sub-style (#3). In latent space, all three churches are close together, hence suggesting similar form and possible architectural-cultural relationship. However, we can see that the model correctly placed both Lemko Snynskyi sub-style churches close together in latent space within the same micro-cluster due to their nearly exact same style. Furthermore, church 3 was plotted further away and within a different, though neighboring cluster due to both its slight differences and overlapping features. When plotting them to geographic space, they appear noticeably close together, suggesting potential cultural overlap. Though morphologically similar, closer inspection revealed subtle differences including differing front facades, roof shapes, and bell tower geometries. Interestingly, all three churches are located within close proximity to each other and right around the border of both the Lemko and Transcarpathian style regions. Their similar form may be the result of the cultural overlap and blurring that occurs at these ethnic boundary zones. Based on this micro trend, churches in these regions could be classified in a new way, such as being a “border style” which can fall within multiple primary style groups at once.
An unusual cluster includes a large group of churches that at first glance only seemed tangentially related by their hip roof configurations and minimal planar exterior walls. More noticeable were their differences, being a seemingly random collection of churches of different styles. Nevertheless, latent space suggested they were very similar given their densely latent space grouping. After investigating, it was suspected that the clustering may have been caused by the presence of noticeable building additions added to both sides of the backs of these churches. These later addi-tions gave the churches a “T” configuration, which for all schools of temple is highly unusual. However, the model appeared to have considered this unusual characteristic as an important “defining” feature, thus leading to the creation of this cluster . Given this, clusters should always be rigorously investigated and supplemented with the proper domain knowledge to detect these issues.
Another unusual cluster was located in the far-left corner of the latent distribution and contained churches without towers (Figure 184). This characteristic was featured among all churches within the cluster, thus indicating a potential sub-style of church. However, when referring to previous research[25], it was quickly discovered that almost none of these churches belonged to the same sub-style or primary style category. Rather, they were a hodgepodge of different styles located within different regions. Furthermore, the size of these churches varied greatly and in one case, it was discovered that one of the churches originally had a tower which had since burned down. With this in mind, the cluster did not provide any particu larly useful knowledge other than the shared towerless feature. Thus, it is imperative to critically examine the contents of each cluster before making assumptions and drawing conclusions.
When referring to the geographic distribution of churches, it also cannot be assumed that churches within close proximity of each other necessarily have similar morphological traits. Take for example, the three Transcar-pathian churches shown below (1,2,3). Though they have nearly identical forms and are located along the same stretch of river valley, other nearby churches just one valley over (churches 4,5,6), are Boyko style, an entirely different School of folk architecture construc tion. However, we can see that our DL process has correctly placed these two styles of churches on seperate sides of the latent space distribution given there drastic differences in form. Given this, one cannot assume that churches within close proximity are similar in style. Thus, our model provides a means of quickly identify potential mismatches.
This research presents a detailed case study of a critical engagement with building data and Deep Learning techniques for the purposes of architectural-historical form analysis. This thesis argues that insightful morphological relationships among our dataset of 313 Carpathian wooden churches might be revealed using contemporary DL methods and reflects upon specific ways these analyses might enrich existing architectural scholarship and expert knowledge on the subject.
To begin, I compare DL-informed results with conventional studies [25], that have utilized traditional analytical methods and demonstrate that there is correlation between the way in which these two differing approaches have grouped churches in terms of similar form. I then show how DL techniques can help to identify groups of churches that share similar forms according to both general and specific architectural features by altering cluster size. This allows both primary, sub-style, and mi cro-style groups of churches to be identified. Additionally, I provide evidence that DL-techniques can help to identify and located clusters of harder to recognize endemic micro-styles that have developed within isolated geographic regions, such as within certain valleys or along certain rivers. Additionally, I suggest how DL methods might allow us to understand how certain styles morph over geographic space by highlighting how certain churches take on more or less of a particular style according to their dis tance to that style regions center. I also highlight how DL-based findings can potentially be misleading if not properly interpreted and supplemented with domain knowledge and existing research. Finally, I offer a path for DL-based form analysis techniques to be put in conversation with traditional architec tural studies in order to help expand and enhance our understanding of architecture.
Finally, my thesis offers a number of useful technical contributions to both the field of archi tectural form-analysis and to the research of Transcarpathian wooden churches. First, it demonstrates a method to build a custom 3D dataset of a non-western, generally rural, and “niche” building type using the NeRS technique [23] that converts sparse imagery into three-dimensional models. Second, it demonstrates how similarly small and custom datasets can be successfully augmented in size in order to sufficiently train DL-models. Thirdly, it presents a multi-modal approach for analyzing latent space distributions by incorporating both DL-oriented approaches, such as Rhee’s method of clustering and identifying typical and least-typical form, and traditional approaches such as reference to Y. Taras’s cultural-zoning map, church style and sub-style diagrams.
Figure 186: Transcarpathian style Church of the Holy Spirit (1892) in the village of Bystryi, Ukraine with the ground truth loca tion of all Transcarpathian churches within latent space. Image sourced from M. Syrokhman. Diagram by author.
By having access to existing research, domain expert, geographic mapping, and a comprehensible small da-taset, I was able to obtain a deep and rich understanding of our data, evaluate the findings of our work and discov-er novel insight, through the following observations:
1. How morphology indicative of the four main schools of folk temple buildings (Boyko, Lemko, Tran scarpathian, and Hutsul) was dispersed among the latent space distribution using primary clustering.
2. How secondary clustering of smaller groups of churches generally provided better identification of church sub-styles within more defined geographic areas.
3. How micro-clustering of even smaller church groupings helped to identify both endemic micro-styles that could only be found in certain valleys or along specific rivers and also interesting border styles that contained a synthesis of two or more schools of folk architecture construction.
This rigorous understanding allowed me to then identify and establish broader patterns across the data including:
1. The multi-directional manner in which tower height and count change across latent space distribution.
2. The degree and direction building forms change from tripartite to cruciform across the latent space distribution.
3. How the dispersion of church datapoints within our latent space distribution diagram is generally organized into two overarching master groups being churches with single towers over the front entry space, and churches with towers over the middle nave space
A thorough understanding of the building data also allowed me to identify subtle micro form patterns within the latent distribution that indicated:
1. A unique and nuanced condition where tower height over the narthex space changes amongst various Lemko sub-style church categories
2. A rare instance when the church form of two opposing Schools of folk architecture completely overlaps and are almost indistinguishable from one another
3. How certain styles morph over geographic space by highlighting how certain hybrid-style churches take on more or less of a particular primary style according to their distance to that style regions center.
4. How Boyko church forms transition into Lemko church forms and vice versa.
5. How Trascarpathian church forms transition into Lemko church forms and vice versa.
Lastly, various potential misleading clusters were identified within the latent distribution including:
1. A cluster containing a hodgepodge collection of different style churches that did not have towers.
2. A cluster containing stylistically unrelated churches defined by building additions that resulted in the churches having a “T” formation
3. A number of outlier styles that tended to cluster together regardless of major style differences.
The use of DL-based techniques for architectural form analysis has clear strengths but should not be used as a stand-alone tool in its current state. Alone and without prior knowledge of the dataset, we run the risk of misinterpreting results, mak ing assumptions, and drawing inaccurate conclusions. Rather, by complimenting DL-methods with findings from existing research, domain knowledge, and geographic reference, we can more accurately interpret its findings and draw more meaningful conclusions. This became quite clear during study as I had to continually shift my attention between latent space, map space, and existing research in order to accurately evaluate, question and investigate what was learned by our model and whether any new or meaningful knowledge could be obtained from the results. As these churches are the result of a complex synthesis of culture, religious belief, community needs, and often can only be differentiated by subtle differences, any findings based on form alone are insufficient when trying to establish clear styles or sub-styles. Rather, work of this kind should be used as just one of various methods to help us expand our knowledge of these beautiful, yet complex works of architectural heritage. This is especially relevant to the Carpathian wooden churches of Eastern Europe as there still is “no unanimous opinion about types of churches and criteria they should be attributed to the schools of the national temple building” [25], due in part to the “lack of comprehensive studies of building types using the cartographic method[s], [for example, by] drawing hundreds of plans, facades and putting them on thematic maps in order to identify the geography of the spread of characteristic architectural solutions and the boundaries of architectural and cultural dis tricts” [25]. However, as DL-learning techniques provide a means of doing just this, I believe researchers of this building type and others could greatly benefit from this new and informative method of DL-based form analysis.
In this research, I also demonstrated how a custom 3D dataset of a specific architectural type can be created us ing the NeRS technique. This is important as 3D architecture data in general, is mostly unavailable online, especially of a niche, rural and rare building type like Carpathian wooden churches. By building this dataset, I hope to have demonstrated how creating custom 3D data for DL-based research is not impossible and does not require on-site scanning but can be cre ated through sparse photographs alone. This is especially relevant when buildings are not accessible, for example during times of conflict, such as the ongoing Russian invasion of Ukraine, are difficult or impossible to reach, are too dispersed or are too numerous to document by hand. In addition, buildings are often temporary or even ephemeral objects that do not permanently exist. This is quite apparent with the Carpathian wooden churches which have disappeared drastically over the past 100 years and continue to do so today due to neglect, arson, political reasons, or destruction during times of war. However, creating a dataset using NeRS was tedious and time consuming given the amount of manual work required for each one of the thousands of images. This was reflected in the multiple months it took to build the Carpathian wooden church dataset.
In addition to 3D reconstruction, by encoding the style of these churches using DL-techniques like VAE I was able to preserve both the individual and overall styles present within the dataset. As the morphology of the dataset is repre sented as a distribution of latent vectors, both existing churches and novel churches in similar styles can be generated, thus providing a means to capture and provide access to the stylistic “essence” of Carpathian wooden churches far beyond their potential physical lifespan. When considering the threat to Ukrainian material culture given the current war with Russia, digitally preserving and protecting both the churches themselves and their stylistic “essence” becomes increasingly important if we hope to preserve this important and irrefutably distinct part of Ukrainian architectural folk heritage.
Figure 188: Lemko style Temple of the Protection of the Mother of God in the village of Miroľa Slovakia with the ground truth location of all Lemko churches within latent space. Image sourced from Image from www.iabsi.com/gen/public/settlements/SL_Dobroslava.htm. Diagram by author.
The demonstrated study only encoded a single primary style of wooden church, being the Carpathian style and closely related surrounding styles. It may be interesting to include other wooden church styles from more distant territories, for example Norwegian wooden churches, in order to observe their place within latent space in comparison to Carpathian wooden church. This would be a fruitful way to understand formal relationships and reveal interesting patterns across the broader building styles of more contrasting ethnographic zones.
During the data preparation stage, the 3D data was also highly abstracted when being converted into a 32x32x32 voxel. As a result, the VAE model did not have the opportunity to capture and encode more subtle form characteristics. As a result, buildings that may have had subtly different forms were clustered too closely together due to this lack in resolu tion. In addition, ground truth building dimensions were likely distorted during the 3D reconstruction process due to its reliance on sparse imagery. As a result, buildings encodings were based on both abstracted and distorted representations. In future studies, I could address this by supplying more images in the 3D reconstruction process to reduce distortion and by converting our data to 64x64x64 voxels to decrease abstraction. Overall, by increasing 3D reconstruction accuracy and reducing distortion during the data conversion stage, findings could be enhanced through increased representational accuracy and thus more accurate form encodings and robust latent space distributions.
Selecting churches to include in the dataset was also done early on in the study, prior to gaining a more informed understanding of the range of Carpathian wooden church styles that exist. In that regard, I may have included within or left out churches from our dataset that shouldn’t have been. One result of this was that a number of churches from the Opilska cultural region were mistakenly added to the dataset thinking that they were Boyko style due to their similar mor phology. Interestingly however, the model was seemingly able to tell the difference between the two, with Opilska church es being more clustered near the bottom left while Boyko churches tended to be clustered above at middle left. However, it is unknown how much this mistakenly included style had affected the overall organization of the dataset. In the future, more expert knowledge could be accessed earlier on to ensure the data is as refined as possible.
The model used in this research also had trouble adequately encoding the less represented church styles within in the dataset due to the lack in volume of similar styled examples. As a result, these particular churches were placed either within small clusters of other outlier churches at the perimeter of the latent space distribution or mixed in with other groups which they only appeared to be loosely associated with. In addition, when decoding their z-values, their resultant decoded forms were often inaccurate and were more reflective of other styles that were better represented, volume-wise within the dataset. This suggested that these churches were inaccurately represented by the model, which in turn, may have affected results through their inclusion in incorrect clusters. This may explain why clusters contained churches that seemingly didn’t match any of the typical morphological characteristics dominant within the group. To address this in fu ture work, I could increase the size of our dataset with more churches to reduce outlier cases our better augment our data to compensate for them.
Data augmentation could also be improved by introducing more randomness and distortion. Within the study, I only used random building scaling in the x, y, and z direction and continuous rotation from -16 degrees to +16 degrees in 2-degree increments. Future studies could introduce more varied kinds of distortion such as slight building warping, scaling of cer tain parts of buildings, or introduction of geometric noise. This may help improve model training by reducing reconstruc tion error, increase reconstruction accuracy, and create a better partitioned and distributed latent space.
Next, the results of this experiment were only explored and evaluated by members of this team, thus the number of morphological patterns across the latent space distribution that could be investigated was limited. In future studies, I could share results with others, such as domain experts or even members of the community where the churches are located. This may reveal novel relationships that may have not been missed or gone unnoticed. This could be done through an online website that allows for simple and straightforward means to explore the latent space and its relation to, geographic space and 3D models. Metadata and existing research could also be made easily available. By providing an interactive interface with easy-to-use controls, it is suspected that many interesting findings could be obtained through this type of community involvement.
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Michael Hasey is a computational designer who works at the intersection of artificial in telligence and architectural design and is currently pursuing a Master of Computational Design degree at Carnegie Mellon University. He holds a Bachelor of Architecture degree (HBAS) from the University of Waterloo and a Master of Architecture degree (M. ARCH) from McGill University. With over seven years of professional experience working within both the architecture and technology industries, he continually seeks out new ways to blend the humanism of traditional design with the rigors of contemporary computer sci ence-based techniques. His current research explores new opportunities for deep learn ing-based methods to codify, analyze and generate complex and culturally rich architec tural forms in new ways. He currently lives in Pittsburgh with his wife Erica.
Daniel Cardoso Llach is an architect and researcher interested in issues of automation in design, human-machine interaction, interdisciplinary creativity, and technological cul tures in architecture and design. He is an Associate Professor in the School of Architec ture at Carnegie Mellon University, where he chairs the Master of Science in Compu tational Design program and co-directs CodeLab. He is the author of the book Builders of the Vision: Software and the Imagination of Design, which identifies and documents the theories of design emerging from postwar technology projects at MIT, and traces critically their architectural repercussions. His writings have been published in journals including Design Issues2 Architectural Research Quarterly (ARQ), Digital Creativity, and Thresholds, among others, and in several edited collections including The Active Image: Architecture and Engineering in the Age of Modeling (Springer 2017) and DigitalSTS: A Handbook and a Fieldguide (Princeton 2019). Daniel routinely lectures and teaches workshops around the world. He holds a Bachelor of Architecture from Universidad de los Andes, Bogotá, and a PhD and MS (with honors) in Design and Computation from MIT. He has also been a research fellow at Leuphana (MECS), Germany, and a visiting scholar at the University of Cambridge, UK.
Jinmo Rhee
Jinmo Rhee is a computational designer and architect interested in the integration of artificial intelligence and space design. Jinmo holds an MS in Computational Design from Carnegie Mellon University, and is currently pursuing the PhD in Computational Design. He received his Bachelor of Architecture from Korea National University of Arts (KNUA) and completed the Royal Institute of British Architects (RIBA) part I and part II. He works with generative systems and programming to explore and analyze spatial quality and to implement technologies based on artificial intelligence that bridge urban and architectural design. Currently, Jinmo is investigating the application of machine learning in architectural and spatial design with “contextness” and expanding his borders to include exhibitions, education, and publication.
Jason Zhang is a PhD student at the Robotics Institute at Carnegie Mellon University and is co-advised by Professors Deva Ramanan and Shubham Tulsiani. In 2021, he developed and release NeRS as part of his PhD research. He is also supported in part by an NSF Graduate Research Fellowship. Jason completed his undergraduate degree at UC Berkeley where he worked on single-view 3D human mesh pose estimation and prediction with Professor Jitendra Malik and Angjoo Kanazawa, and robot learning from user-given corrections with Professor Anca Dragan. While at Berkeley, he helped de velop Prob 140, an upper-division computational probability course for Berkeley’s Data Science Major. Here, he served as the Head Teaching Assistant twice.
Mykhailo Syrokhman is an art critic, artist, professor, and leading researcher of wooden church architecture in Transcarpathia. He graduated from the Faculty of Romano-Ger manic Philology of Uzhhorod State University in 1977 and the art studio of Zoltan Bako nia, a renowned artist in Western Ukraine. Since 1989, he has worked at the Uzhgorod College of Arts and, since 2009, at the Transcarpathian Academy of Arts. He is author of more than 30 texts for albums and catalogs of Transcarpathian artists and dozens of arti cles as well as laureate of the regional award in the field of fine arts (art science) in 2000, 2009, 2012 and 2014. Based on twenty years of field research and archival research, he published the monumental monograph “Churches of Ukraine. Transcarpathia” (2000), books “Lost churches of Transcarpathia” (1999), “55 wooden churches of Transcarpath ia” (2008), “Reformed churches of Transcarpathia” (2001), “Greek-Catholic churches of Transcarpathia” (2002), booklets from series “Wooden Churches of Transcarpath ia” (2005), “Wooden Churches and Bell Towers of Transcarpathia” (2016), “Builders of Transcarpathian Churches” (2019), “Lost Wooden Churches of North-Eastern Slovakia” (2019, co-authored with Ya. Dzhoganykom), and most recently, “The Lost Churches of Transcarpathia” (2022).
Information about each of the 331 individual churches within the dataset can be found in the following section. First, a labelled latent space diagram indicates the position of each encoded church form according to a unique identification number assigned to each building. The specific color of each point refers to its ground truth primary style (School of folk architecture construction).
Each church is mapped to its geographic position on a large map of the central Carpathian mountain region. Each church can be located by its unique identification number. Churches are assigned the same number in both latent and map space. The color of each church point also refers to its ground truth primary style (School of folk architecture construction).
Boyko
Hutsul
Lemko
Transcarpathian Pokut
Bukovyna
Pre-Carpathian Podnistrovska Opilska Marmures
Podilia
Volyn
Transylvania Bessarabia South Malapolska Gothic Unkown
Finally, each of the 331 individual churches are described in detail within a large data chart. Here, each church is assigned an identification number, represented in its reconstructed 3D form, and general information is recorded such as name, construction date, address, style, and so on. In addition, a small thumbnail image for each church records its specific geo graphic location on a map as well as its position in latent space as a large color dot. Dot colors are ambigous and do not refer to any specific church descriptor.
0
3
4
Church of the Assump tion 1937 Standing Transcarpathian
Church of the Assump tion 1852 Standing Boyko
Church of St. the great martyr Dmitry 1780 Standing Transcarpathian
5
Church of St. Nicholas 1750 Standing Transcarpathian
6
Church of the Holy Spirit 1700* Standing Transcarpathian
Church of the Nativity of the Virgin 1692 Standing Boyko 8
7
Church of St. Nicholas 1704 Standing Transcarpathian
48.5797, 23.5012
Mizhhiria / Міжгірська Khust Raion / Хустський район
49.661, 22.835
Dobromyl / Добромильська
48.5815, 23.446 Repynne / Репинне
48.6431, 23.428 Holiatyn / Голятин
Sambir Raion / Самбірський район
Zakarpattia Oblast / Закарпатська область
Ukraine / Україна Mykhailo Syrokhman
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
Mizhhiria / Міжгірська Khust Raion / Хустський район
Mizhhiria / Міжгірська
Khust Raion / Хустський район
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Maxim Ritus
48.6992, 23.2317 Huklyvyi / Гукливий Volovets / Воловецька
49.4255, 23.8318
Krynytsia / Криниця
Mykolaiv / Миколаївська
Mukachevo Raion / Мукачівський район
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Maxim Ritus
Zakarpattia Oblast / Закарпатська область
Stryi Raion / Стрийський район
48.1359, 23.5081
Oleksandrivka / Олександрівка
Khust / Хустська Khust Raion / Хустський район
Lviv Oblast / Львівська область
Ukraine / Україна Maxim Ritus
Zakarpattia Oblast / Закарпатська область
Ukraine / Україна Maxim Ritus
Ukraine / Україна Maxim Ritus
13
Church of St. arch. Michael 1874 Standing Boyko 14
Church of St. Nicholas 1700* Standing Transcarpathian 15
Church of St. Michael 1745 Standing Boyko 16
Church of St. Anne 1791 Standing Transcarpathian 17
Church of St. Basil (upper) 1776 Standing Transcarpathian 18
Church of St. Nicholas 1604 Standing Transcarpathian 20
Church of the Presen tation of the Blessed Virgin 1734 Standing Transcarpathian 21
Church of the Epiphany 1693 Standing Boyko 23
Church of St. Apostle Andrew 1728 Standing Przemysl 24 St. Nicholas the Won derworker 1798 Standing Transcarpathian
48.9028, 23.1816 Smozhe / Сможе Koziova / Козівська Stryi Raion / Стрийський район
48.8484, 22.6078 Chornoholova / Чорноголова
ДубриницькоМалоберезнянська / ДубриницькоМалоберезнянська
Uzhhorod Raion / Ужгородський район
Lviv Oblast / Львівська область
Ukraine / Україна Maxim Ritus
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Maxim Ritus
48.984, 22.8546 Uzhok / Ужок Stavne / Ставненська
ДубриницькоМалоберезнянська / ДубриницькоМалоберезнянська
47.9999, 23.8353 Nyzhnia Apsha / Нижня Апша
Uzhhorod Raion / Ужгородський район
Uzhhorod Raion / Ужгородський район
Solotvyno / Солотвинська
47.9999, 23.8353 Nyzhnia Apsha / Нижня Апша Solotvyno / Солотвинська
Tiachiv Raion / Тячівський район
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Maxim Ritus
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Maxim Ritus 48.8243, 22.6731 Bukivtsovo / Буківцьово
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Maxim Ritus
Tiachiv Raion / Тячівський район
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Maxim Ritus
48.4249, 22.9845 Lokit / Локіть Irshava / Іршавська Khust Raion / Хустський район
49.6908, 24.0422 Kuhaiv / Кугаїв Solonka / Солонківська
Lviv Raion / Львівський район
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Maxim Ritus
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
49.6805, 22.8297 Patskovychi / Пацьковичі
Dobromyl / Добромильська
48.6494, 23.3675 Izky / Ізки Pylypets / Пилипецька
Sambir Raion / Самбірський район
Khust Raion / Хустський район
Lviv Oblast / Львівська область
Ukraine / Україна Maxim Ritus
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Maxim Ritus
Church of St. Basil the Great 1733 Standing Transcarpathian 27
Vvedenskaya Church 1759 Standing Transcarpathian 29
Church of St. Spirit 1760 Standing Transcarpathian 30
Church of St. Nicholas 1650 Standing Boyko 31
Church of St. Pantelei mon 1872 Standing Opilska 33
Church of St. Paraskevi 1600* Standing Boyko 34
Church of the Cathe dral of the Blessed Virgin (St. Demetrius) 1838 Standing Boyko 37
Church of the Exaltation of the Holy Cross 1859 Standing Hutsul 39
Church of the Epiphany 1755 Standing Boyko 40
Church of St. Paraskevi 1865 Standing Hutsul
48.7641, 23.0689 Zadilske / Задільське
Nyzhni Vorota / Нижньоворітська
48.7047, 23.2996 Roztoka / Розтока Pylypets / Пилипецька
Mukachevo Raion / Мукачівський район
Khust Raion / Хустський район
Zakarpattia Oblast / Закарпатська область
Ukraine / Україна Maxim Ritus
Zakarpattia Oblast / Закарпатська область
48.8236, 23.0584 Бистрий / Бистрий
Nyzhni Vorota / Нижньоворітська
Mukachevo Raion / Мукачівський район
49.2241, 23.8748 Slobidka / Слобідка Stryi / Стрийська Stryi Raion / Стрийський район
Ukraine / Україна Maxim Ritus
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Maxim Ritus
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
49.564, 23.907 Honiatychi / Гонятичі Mykolaiv / Миколаївська Stryi Raion / Стрийський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus 49.0357, 23.5136 Skole / Сколівська Stryi Raion / Стрийський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus 48.9156, 23.1095 Matkiv / Матків Koziova / Козівська Stryi Raion / Стрийський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus 48.4032, 25.0641 Mykytyntsi / Микитинці Kosiv / Косівська Kosiv Raion / Косівський район
Ivano-Frankivsk Oblast / Івано-Франківська область Ukraine / Україна Maxim Ritus
49.1211, 23.8968 Lysovychi / Лисовичі Morshyn / Моршинська Stryi Raion / Стрийський район Lviv Oblast / Львівська
Ivano-Frankivsk Oblast / Івано-Франківська
Ukraine / Україна Maxim Ritus
Ukraine / Україна Maxim Ritus 48.4663, 24.9427 Velykyi Kliuchiv / Великий Ключів Nyzhnii Verbizh / Нижньовербізька Kolomyia Raion / Коломийський район
41
Church of St. Nicholas 1862 Standing Hutsul 42
Church of the Assump tion of the Blessed Virgin 1776 Standing Pokut 43
Church of St. Arch. Michael 1756 Standing Hutsul 44
Church of the Trans fer of the Relics of St. Nicholas 1886 Standing Hutsul 47
Church of the Trans fer of the Relics of St. Nicholas 1868 Standing Hutsul 48
Holy Ascension Church 1450 Standing Bukovyna 49
Church of the Assump tion 1739 Standing Hutsul 50
Church of St. Michael the Archangel 1818 Standing Transcarpathian 51
Church of the Holy Spirit 1795 Standing Transcarpathian 52
Church of St. Yura 1650* Standing Pre-Carpathian Podnistrovska
48.469, 25.168 Debeslavtsi / Дебеславці
Mateivtsi / Матеївецька
48.3526, 25.7146 Dubivtsi / Дубівці Mamaivtsi / Мамаївська
Kolomyia Raion / Коломийський район
Ivano-Frankivsk Oblast / Івано-Франківська область
Chernivtsi Raion / Чернівецький район
48.8942, 24.1675 Tsineva / Цінева Duba / Дубівська Kalush Raion / Калуський район
Chernivtsi Oblast / Чернівецька область
Ukraine / Україна Maxim Ritus
48.8905, 24.0626 Spas / Спас Spas / Спаська Kalush Raion / Калуський район
Ivano-Frankivsk Oblast / Івано-Франківська область
Ukraine / Україна Maxim Ritus
Ivano-Frankivsk Oblast / Івано-Франківська область
Ukraine / Україна Maxim Ritus
Ukraine / Україна Maxim Ritus
48.4116, 25.4771 Dolishnie Zaluch chia / Долішнє Залуччя Sniatyn / Снятинська Kolomyia Raion / Коломийський район
Mamaivtsi / Мамаївська
48.6721, 24.5309 Hvizd / Гвізд Nadvirna / Надвірнянська
Chernivtsi Raion / Чернівецький район
Nadvirna Raion / Надвірнянський район
Chernivtsi Oblast / Чернівецька область Ukraine / Україна Maxim Ritus
Ivano-Frankivsk Oblast / Івано-Франківська область Ukraine / Україна Maxim Ritus 48.3638, 25.7787
48.4513, 23.6596 Nehrovets / Негровець
Kolochava / Колочавська
48.4388, 23.7452
Kolochava / Колочавська
Khust Raion / Хустський район
Ivano-Frankivsk Oblast / Івано-Франківська область Ukraine / Україна Maxim Ritus
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Maxim Ritus
Khust Raion / Хустський район
49.3477, 23.4992
Drohobych / Дрогобицька
Drohobych Raion / Дрогобицький район
Zakarpattia Oblast / Закарпатська область
Ukraine / Україна Maxim Ritus
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
53
Church of the Exaltation of the Holy Cross 1613 Standing Pre-Carpathian Podnistrovska 54
Church of the Holy Great Martyr Paraskeva 1815 Standing Boyko 55
Church of St. John the Theologian 1782 Standing Volyn 56
Church of St. Nicholas 1782 Standing Volyn 57
Church of the Exaltation of the Holy Cross 1731 Standing Unknown 58
Church of St. Arch. Michael 1645 Standing Pokut 60
Church of the Ascension 1630 Standing Boyko 61
Church of St. Trinity 1850 Standing Volyn 62
Churches of the Position of the Belt of the Moth er of God 1894 Standing Boyko 64
Church of the Resur rection 1870 Standing Volyn
49.3502, 23.497
Drohobych / Дрогобицька
49.352, 23.4892
Drohobych / Дрогобицька
50.4308, 24.5462 Fusiv / Фусів Sokal / Сокальська
Drohobych Raion / Дрогобицький район
Drohobych Raion / Дрогобицький район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
Chervonohrad Raion / Червоноградський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
50.4326, 24.5565 Kniazhe / Княже Sokal / Сокальська
Chervonohrad Raion / Червоноградський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus 49.2999, 23.8585 Dobrivliany / Добрівляни Stryi / Стрийська Stryi Raion / Стрийський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus 48.7793, 24.3851 Rosilna / Росільна Dzvyniach / Дзвиняцька
Ivano-Frankivsk Raion / ІваноФранківський район
Ivano-Frankivsk Oblast / Івано-Франківська область Ukraine / Україна Maxim Ritus 49.0248, 25.7936 Chortkiv / Чортківська
Chortkiv Raion / Чортківський район
Chervonohrad Raion / Червоноградський район
Lviv Oblast / Львівська область
49.4954, 22.8377 Velyka Sushytsia / Велика Сушиця Khyriv / Хирівська
50.3959, 24.0524
Zhuzheliany / Жужеляни Belz / Белзька
Sambir Raion / Самбірський район
Chervonohrad Raion / Червоноградський район
Lviv Oblast / Львівська область
Ukraine / Україна Maxim Ritus
Ternopil Oblast / Тернопільська область Ukraine / Україна Maxim Ritus 50.4354, 24.5223 Shpykolosy / Шпиколоси Sokal / Сокальська
Ukraine / Україна Maxim Ritus
Lviv Oblast / Львівська область
Ukraine / Україна Maxim Ritus
66
Church of St. Cosmas and Damian 1871 Standing Volyn 67
Church of the Exaltation of the Holy Cross 1864 Standing Volyn 70
Church of the Interces sion 1766 Standing Boyko 71
Church of the Interces sion The Virgin 1820 Standing Boyko 72
Church of St. Michael the Archangel 1738 Standing Volyn 73
Church of the Nativity Ave. The Virgin 1872 Standing Boyko 75
Church of St. Paraskeva 1440 Standing Pre-Carpathian Podnistrovska 76
Church of St. Paraskevi 1833* Standing Boyko 77
Church of the Miracles of St. Arch. Michael 1889 Standing Boyko 78
Church of St. Nicholas 1690 Standing Boyko
50.4208, 24.0576 Tsebliv / Цеблів Belz / Белзька
50.3316, 24.2794 Volsvyn / Волсвин Chervonohrad / Червоноградська
Chervonohrad Raion / Червоноградський район
Chervonohrad Raion / Червоноградський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
49.6533, 22.9375 Deshychi / Дешичі Dobromyl / Добромильська
Sambir Raion / Самбірський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
49.4454, 22.8164 Libukhova / Лібухова Khyriv / Хирівська Sambir Raion / Самбірський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus 50.4574, 24.458 Perviatychi / Перв’ятичі Sokal / Сокальська Chervonohrad Raion / Червоноградський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus 49.4814, 22.7505 Terlo / Терло Khyriv / Хирівська
Sambir Raion / Самбірський район
49.4907, 22.9613 Staryi Sambir / Старосамбірська
49.5646, 22.8309 Piatnytsia / П’ятниця
49.6614, 22.8993
Zorotovychi / Зоротовичі
Dobromyl / Добромильська
Sambir Raion / Самбірський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
Sambir Raion / Самбірський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
Dobromyl / Добромильська
49.2866, 23.0765 Turie / Тур’є Strilky / Стрілківська
Sambir Raion / Самбірський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
Sambir Raion / Самбірський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
79
St. Michael’s Church 1779* Standing Hutsul 80
Church of the Entry into the Temple Ave. The Virgin 1680 Standing Boyko 81
Church of St. Mykola Charnetsky 1900 Standing Transcarpathian 82
Church of the Cathedral Ave. The Virgin 1780 Standing Boyko 83
Church of St. Paraskevi 1781 Standing Boyko 84
Church of St. the proph et Elijah 1698 Standing Pre-Carpathian Podnistrovska 85
Church of St. vmch. Eustace 1792 Standing Boyko 88
Church of St. Michael the Archangel 1793* Standing Podilia 89
Church of St. Nicholas and the Intercession 1752 Standing Podilia 90
Church of Sts. Ap. Peter and Paul 1827 Standing Transcarpathian
49.2763, 22.9808
49.2162, 22.8824
Yasenytsia-Zam kova / ЯсеницяЗамкова
Strilky / Стрілківська
Sambir Raion / Самбірський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
Vovche / Вовче Turka / Турківська
Sambir Raion / Самбірський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
49.2107, 22.9025 Vovche / Вовче Turka / Турківська
Sambir Raion / Самбірський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
49.3794, 22.9946 Busovysko / Бусовисько Strilky / Стрілківська Sambir Raion / Самбірський район
49.3334, 22.9262 Tysovytsia / Тисовиця
Strilky / Стрілківська
Sambir Raion / Самбірський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
Monastyr-Lish nianskyi / МонастирЛішнянський
49.3843, 23.4349
Drohobych / Дрогобицька
Drohobych Raion / Дрогобицький район
Sambir Raion / Самбірський район
48.9151, 28.0034
Telelyntsi / Телелинці Stanislavchyk / Станіславчицька
48.6722, 28.0135 Lozova / Лозова Sharhorod / Шаргородська
Zhmerynka Raion / Жмеринський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus 49.3322, 22.9763 Strilky / Стрілки Strilky / Стрілківська
Vinnytsia Oblast / Вінницька область Ukraine / Україна Maxim Ritus
Zhmerynka Raion / Жмеринський район
48.2695, 24.419 Lazeshchyna / Лазещина Yasinia / Ясінянська Rakhiv Raion / Рахівський район
Vinnytsia Oblast / Вінницька область Ukraine / Україна Maxim Ritus
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Maxim Ritus
91
Church of the Assump tion of the Blessed Virgin 1730 Standing Boyko 92
Church of St. Arch. Michael 1791 Standing Boyko 93
Church of the Nativity of the Blessed Virgin 1600* Standing Boyko 94
Church of St. arch. Michael 1857 Standing Boyko 95
Church of the Trans fer of the Relics of St. Nicholas 1780 Standing Boyko 96
Church of the Assump tion Ave. The Virgin 1750 Standing Boyko 97
Church of St. Nicholas 1739 Standing Boyko 98
St. Michael’s Church 1663 Standing Pre-Carpathian Podnistrovska 99
Church of St. Covers 1792 Standing Lemko 100
St. Michael’s Church 1769 Standing Podilia
49.3149, 23.0278 Topilnytsia / Топільниця Strilky / Стрілківська
49.3008, 22.9244 Strilky / Стрілки Strilky / Стрілківська
Sambir Raion / Самбірський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
Sambir Raion / Самбірський район
49.4428, 22.784 Terlo / Терло Khyriv / Хирівська
Sambir Raion / Самбірський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
49.4545, 22.8534 Bilychi / Біличі Staryi Sambir / Старосамбірська
Sambir Raion / Самбірський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus 49.1526, 23.0377 Turka / Турківська Sambir Raion / Самбірський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus 49.1665, 22.9947 Turka / Турківська Sambir Raion / Самбірський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus 49.1614, 23.0145 Turka / Турківська Sambir Raion / Самбірський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus 49.224, 23.1113 Isai / Ісаї Turka / Турківська Sambir Raion / Самбірський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus 50.3606, 30.5177 Ukraine / Україна Maxim Ritus 49.0828, 27.0654 Zinkiv / Зіньків Zinkiv / Зіньківська Khmelnytskyi Raion / Хмельницький район
Khmelnytskyi Oblast / Хмельницька область Ukraine / Україна Maxim Ritus
101
Nicholas Church 1754 Standing Podilia 102
Church of St. Joseph the Betrothed 1766 Standing Podilia 103
Church of the Nativity of the Blessed Virgin 1811 Standing Hutsul 104
Church of St. Basil the Great 1897 Standing Hutsul 105
Church of St. Basil the Great 1733 Standing Boyko 106
Church of the Resur rection 1715 Standing Podilia 108
Church of the Nativity of the Virgin Mary 1801 Standing Transcarpathian 109
Church of the Nativity of the Virgin 1801 Standing Transcarpathian 110
Church of the Nativity of the Virgin 1643 Standing Transcarpathian 114
Church of the Holy Spirit 1502 Standing Pre-Carpathian Podnistrovska
49.0718, 29.189 Borysivka / Борисівка
Illintsi / Іллінецька Vinnytsia Raion / Вінницький район
49.7333, 30.4637 Zhytni Hory / Житні Гори Rokytne / Рокитнянська
48.284, 24.5704 Vorokhta / Ворохтянська
Bila Tserkva Raion / Білоцерківський район
Vinnytsia Oblast / Вінницька область
Ukraine / Україна Maxim Ritus
Kyiv Oblast / Київська область
Nadvirna Raion / Надвірнянський район
Ivano-Frankivsk Oblast / Івано-Франківська область
Ukraine / Україна Maxim Ritus
Ukraine / Україна Maxim Ritus
49.4286, 24.555 Cherche / Черче Rohatyn / Рогатинська
Ivano-Frankivsk Raion / ІваноФранківський район
Ivano-Frankivsk Oblast / Івано-Франківська область
Ukraine / Україна Maxim Ritus 49.4311, 24.5605 Cherche / Черче Rohatyn / Рогатинська
Ivano-Frankivsk Raion / ІваноФранківський район
49.2276, 25.7468
Derenivka / Деренівка Terebovlia / Теребовлянська
48.6696, 23.3383
Pylypets / Пилипець
48.6696, 23.3383
Pylypets / Пилипець
48.0896, 23.4224
Steblivka / Стеблівка
50.2087, 23.5514
Potelych / Потелич
Ivano-Frankivsk Oblast / Івано-Франківська область Ukraine / Україна Maxim Ritus
Ternopil Raion / Тернопільський район
Pylypets / Пилипецька Khust Raion / Хустський район
Ternopil Oblast / Тернопільська область Ukraine / Україна Maxim Ritus
Pylypets / Пилипецька Khust Raion / Хустський район
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Maxim Ritus
Khust / Хустська
Khust Raion / Хустський район
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Maxim Ritus
Zakarpattia Oblast / Закарпатська область
Rava-Ruska / РаваРуська
Lviv Raion / Львівський район
Lviv Oblast / Львівська область
Ukraine / Україна Maxim Ritus
Ukraine / Україна Maxim Ritus
Church of John the Theologian 1700* Standing Podilia 116
The Church of St. Nicholas 1700* Standing Opilska 117
St. Michael’s Church 1558* Standing Transcarpathian 118
Church of St. Nicholas the Wonderworker 1470* Standing Transcarpathian 119
St. Michael’s Church 1668 Standing Transcarpathian 120
Church of the Transfig uration 1730 Standing Volyn 122
Church of the Assump tion 1603 Standing Boyko 124
Church of St. Dmitry 1775* Standing Podilia 125
Church of the Descent of the Holy Spirit 1671 Standing Boyko 126
Church of St. Nicholas 1870* Standing Opilska
49.5988, 26.1416 Skoryky / Скорики Skoryky / Скориківська
49.8721, 24.9499 Sasiv / Сасів Zolochiv / Золочівська
Ternopil Raion / Тернопільський район
Zolochiv Raion / Золочівський район
Ternopil Oblast / Тернопільська область Ukraine / Україна Maxim Ritus
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
48.5336, 22.9779 Svaliava / Свалявська Mukachevo Raion / Мукачівський район
48.1633, 23.6013 Kolodne / Колодне Uhlia / Углянська Tiachiv Raion / Тячівський район
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Maxim Ritus
48.1433, 23.4556 Danylovo / Данилово Khust / Хустська Khust Raion / Хустський район
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Maxim Ritus
50.6203, 26.7435 Syniv / Синів Hoscha / Гощанська Rivne Raion / Рівненський район
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Maxim Ritus
Rivne Oblast / Рівненська область Ukraine / Україна Maxim Ritus
49.6348, 23.7216 Klitsko / Кліцько Komarno / Комарнівська
Lviv Raion / Львівський район
Ternopil Raion / Тернопільський район
49.5578, 23.1446
Biskovychi / Бісковичі Biskovychi / Бісковицька
50.1122, 24.3431
Kamianka-Buzka / Кам’янка-Бузька
Sambir Raion / Самбірський район
Ternopil Oblast / Тернопільська область Ukraine / Україна Maxim Ritus
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus 49.6427, 26.0821 Koziari / Козярі Skoryky / Скориківська
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
Lviv Raion / Львівський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
127
Church of St. Paraskevi 1708 Standing Opilska 128
Onufrievska church 1680* Standing Opilska 129
Church of the Ascension 1722 Standing Przemysl 130
Church of St. Nikita 1666 Standing Przemysl 131
Church of the Immac ulate Conception The Virgin 1762 Standing Przemysl 132
Church of the Blessed Virgin 1702 Standing Podilia 136
Church of the Nativity of the Virgin 1702 Standing Boyko 137
Church of the Nativity of the Blessed Virgin 1750* Standing Transcarpathian 140
Church of St. Nicholas 1812 Standing Podilia 142
church of the Assump tion of the Blessed Virgin (St. Paraskeva) 1644 Standing Pre-Carpathian Podnistrovska
49.9683, 24.6277
Busk / Буська
Zolochiv Raion / Золочівський район
49.9765, 24.5957 Busk / Буська
49.7981, 23.1587
Mostyska / Мостиська
Zolochiv Raion / Золочівський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
50.0698, 24.3539 Derniv / Дернів Kamianka-Buzka / Кам’янка-Бузька
Yavoriv Raion / Яворівський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
Lviv Raion / Львівський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
49.9426, 24.1795 Vysloboky / Вислобоки Zhovtantsi / Жовтанецька
Lviv Raion / Львівський район
Zolochiv Raion / Золочівський район
49.6978, 24.9418 Pomoriany / Поморянська
Zolochiv Raion / Золочівський район
47.9372, 24.1733 Dilove / Ділове Rakhiv / Рахівська
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus 49.9045, 25.3133 Styborivka / Стиборівка Pidkamin / Підкамінська
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
Rakhiv Raion / Рахівський район
49.0369, 25.0597
Lazarivka / Лазарівка
Monastyryska / Монастириська
49.4289, 23.7502 Medenychi / Меденицька
Chortkiv Raion / Чортківський район
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Maxim Ritus
Ternopil Oblast / Тернопільська область
Ukraine / Україна Maxim Ritus
Drohobych Raion / Дрогобицький район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
143
146
148
chapel of Our Lady of Paramanna 1933 Standing Unknown
Church of St. arch. Michael 1857 Standing Przemysl
Church of St. Arch. Michael 1798 Standing Boyko
Church of the Assump tion Ave. The Virgin 1790* Standing Boyko 153
152
154
155
156
157
Church of the Nativity of the Blessed Virgin 1700* Standing Hutsul
Church “Saint Nicholas” in Glod 1784 Standing Marmures
Church of Saint Nich olas 1754 Standing Marmures
Church Birth of the Mother of God 1611* Standing Marmures
Church of Pious Para schiva 1699 Standing Marmures
158
Church of Assumption 1717* Standing Marmures
49.0894, 23.1028 Ilnyk / Ільник Turka / Турківська
Sambir Raion / Самбірський район
49.4545, 22.8534 Bilychi / Біличі Staryi Sambir / Старосамбірська
49.3958, 22.7976
Sosnivka / Соснівка Staryi Sambir / Старосамбірська
Sambir Raion / Самбірський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
49.1458, 24.9183 Horozhanka / Горожанка Monastyryska / Монастириська
Sambir Raion / Самбірський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
Chortkiv Raion / Чортківський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
Ternopil Oblast / Тернопільська область
Ukraine / Україна Maxim Ritus
48.175, 24.8988 Kryvorivnia / Криворівня Verkhovyna / Верховинська
Verkhovyna Raion / Верховинський район
Ivano-Frankivsk Oblast / Івано-Франківська область Ukraine / Україна Maxim Ritus 47.7249, 24.0786 Glod / Glod
Romania / România Maramures Church Project 47.6902, 24.2684 Bogdan Vodă / Bogdan Vodă Romania / România Maramures Church Project 47.6764, 24.2365 Ieud / Ieud
Romania / România Maramures Church Project 47.6675, 24.1533 Botiza / Botiza Romania / România Maramures Church Project 47.7239, 24.2227 Rozavlea / Ro zavlea Romania / România Maramures Church Project
159
Church of The Holy Archangels Michael and Gabriel 1717* Standing Marmures 164
Church of Pious Para schiva 1639 Standing Marmures 166
Church of Pious Para schiva 1780 Standing Marmures 169
Church of the Birth of the Mother of God 1637 Standing Marmures 170
Church of The Ascen sion of the Lord 1798 Standing Marmures 171
The “baroque” churches of Velykyi Bereznyi 1794 Standing Transcarpathian 173
Boiko church of V. Bereznyi 1631 Standing Boyko 175
Church of St. Michael 1700 Standing Boyko 177
Church of Nova Stu zhytsia 1893 Standing Transcarpathian 179
Boiko Church of Sukhyi 1679 Standing Boyko
47.7349, 24.2261 Rozavlea / Ro zavlea
Romania / România Maramures Church Project 47.761, 23.939 Sârbi / Sârbi Romania / România Maramures Church Project 47.7741, 23.8551 Desești / Desești
Romania / România Maramures Church Project 47.9268, 24.012 Rona de Jos / Rona de Jos
Romania / România Maramures Church Project 47.8211, 24.4361 Poienile de Sub Munte / Poienile de Sub Munte Romania / România Maramures Church Project 48.8531, 22.6089 Chornoholova / Чорноголова
ДубриницькоМалоберезнянська / ДубриницькоМалоберезнянська
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Mykhailo Sy rokhman 48.9421, 22.5902 Kostryna / Кострина Kostryna / Костринська
Uzhhorod Raion / Ужгородський район
Uzhhorod Raion / Ужгородський район
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Mykhailo Sy rokhman 48.951, 22.6846 Vyshka / Вишка Kostryna / Костринська
Uzhhorod Raion / Ужгородський район
Ukraine / Україна Mykhailo Sy rokhman 49.029, 22.5913 Stuzhytsia / Стужиця Stavne / Ставненська
Zakarpattia Oblast / Закарпатська область
Uzhhorod Raion / Ужгородський район
Uzhhorod Raion / Ужгородський район
Zakarpattia Oblast / Закарпатська область
Zakarpattia Oblast / Закарпатська область
Ukraine / Україна Mykhailo Sy rokhman
Ukraine / Україна Mykhailo Sy rokhman 48.9494, 22.7893 Sukhyi / Сухий Stavne / Ставненська
182
Church of Bilasovytsia 1890 Standing Transcarpathian 185
Church of St. Michael the Archangel 1800* Standing Transcarpathian 187
Unknown Wooden Church Standing Transcarpathian 192
Church of St. Je hoshaphat 1908 Standing Hutsul 193
Church of Nativity Standing Opilska 196
Unknown Wooden Church Standing Volyn 199
Unknown Wooden Church Standing Marmures 200
Unknown Wooden Church Standing Transylvania 203
Church of St. Nicholas Standing Bukovyna 206
Voronet Monastary Standing Bukovyna
48.8328, 23.0517
Bilasovytsia / Біласовиця
48.5504, 22.5822
Nyzhni Vorota / Нижньоворітська
Mukachevo Raion / Мукачівський район
Velyki Luchky / Великолучківська
48.618, 22.2877
Uzhhorod / Ужгородська
Mukachevo Raion / Мукачівський район
Zakarpattia Oblast / Закарпатська область
Ukraine / Україна
Zakarpattia Oblast / Закарпатська область
Uzhhorod Raion / Ужгородський район
49.414, 24.2538 Zhyrova / Жирова Khodoriv / Ходорівська Stryi Raion / Стрийський район
Zakarpattia Oblast / Закарпатська область
Mykhailo Sy rokhman
Ukraine / Україна Google Maps
Ukraine / Україна Google Maps
Lviv Oblast / Львівська область Ukraine / Україна Google Maps
49.5207, 24.2072 Borusiv / Борусів Khodoriv / Ходорівська Stryi Raion / Стрийський район
49.4895, 25.2954 Plotycha / Плотича Kozova / Козівська
Ternopil Raion / Тернопільський район
Lviv Oblast / Львівська область Ukraine / Україна Google Maps
Ternopil Oblast / Тернопільська область Ukraine / Україна Google Maps
47.6167, 24.7827 Romania / România Google Maps 46.9685, 25.7307 Tulgheș / Tulgheș Romania / România Google Maps
47.2378, 26.0704 Pipirig / Pipirig Romania / România Google Maps
47.5171, 25.8642 Parcare 2 Voronet / Parcare 2 Vo ronet Romania / România Google Maps
207
Church of Holy Kings Standing Bukovyna 208
Church Assembly Arbore Standing Bukovyna 209
Unknown Wooden Church Standing Bukovyna 210
Church of St Demetrius Standing Bukovyna 211
St. Vovidenia Monastery Standing Bessarabia 212
Church in Lemn Standing Bessarabia 213
Church of Pious Para scheva Standing Bessarabia 214
Church of Saint Nich olas Standing Bessarabia 215
Church of St. George Standing Bessarabia 216
Church in Nadasa Standing Transylvania
47.6481, 25.9063 Pârteștii de Sus / Pârteștii de Sus Romania / România Google Maps 47.7331, 25.929 Arbore / Arbore Romania / România Google Maps 47.6561, 25.9884 Humoreni / Hu moreni Romania / România Google Maps 47.7309, 26.2942 Adâncata / Adân cata Romania / România Google Maps 46.6143, 26.762 Luncani / Luncani Romania / România Google Maps 46.4759, 26.4882 Romania / România Google Maps 46.2838, 26.6023 Romania / România Google Maps 45.8901, 26.6848 Păulești / Păulești Păulești / Păulești
Romania / România Google Maps 46.273, 26.6186 Pârâu Boghii / Pârâu Boghii Romania / România Google Maps 46.7073, 24.8133 Nadășa / Nadășa Romania / România Google Maps
217
Church of St. Cross Standing Transylvania
218
Church in Reghin Standing Transylvania 219
Church in Sacalu de Padure 1809 Standing Transylvania 220
Church of Archangel Michael & Gabriel 1788 Standing Transylvania 221
Church of Archangel Michael & Gabriel 1331* Standing Transylvania 222
Church in Sarata 1755 Standing Transylvania 223
Church of St. Prophet Elijah 1311* Standing Transylvania
224
Church of St. Michael the Archangel Standing Transylvania
Church of St. Arch angels Michael and Gabriel 1700* Standing Transylvania 227
225
Church of St. Nicholas Standing Marmures
46.9402, 25.2335 Stânceni / Stân ceni Romania / România Google Maps 46.7868, 24.7083 Romania / România Google Maps 46.8855, 24.7099
Săcalu de Pădure / Săcalu de Pădure Romania / România Google Maps 46.9201, 24.3056 Sălcuța / Sălcuța Romania / România Google Maps 46.9407, 24.3261 Bungard / Bun gard Romania / România Google Maps 47.071, 24.4369 Sărata / Sărata Romania / România Google Maps 47.0933, 24.6288 Petriș / Petriș Romania / România Google Maps 46.5505, 24.5697
Targu Mures Metro politan Area / Zona Metropolitană Târgu Mureș Romania / România Google Maps
47.3, 24.4527 Rebrișoara / Rebrișoara Romania / România Google Maps 47.7316, 23.9442 Budești / Budești Romania / România Google Maps
229
Church in Sapanta Standing Marmures 232
Church of St. Elijah Standing Hutsul 236
Church of St. Paraskeva 1428 Standing Hutsul 239
Church of St. Nicholas 1774 Standing Bukovyna 243
Church of the Assump tion of the Blessed Virgin Standing Pokut 244
Church of Sts. Arch. Michael and Gabriel 1901 Standing Hutsul 245
Church of the Nativity of the Blessed Virgin Standing Pokut 247
Church of St. Trinity 1865 Standing Pokut 249
Church of St. Demetrius 1746 Standing Hutsul 250
Church of the Holy Prophet Elijah 1938 Standing Hutsul
47.9724, 23.6975 Săpânța / Săpânța Romania / România Google Maps
48.0235, 25.2987 Долишній Шепіт / Долишній Шепіт
48.1986, 25.4161
Berehomet / Берегометська
Vyzhnytsia Raion / Вижницький район
Berehomet / Берегометська
48.2922, 25.9439
Chernivtsi / Чернівецька
Vyzhnytsia Raion / Вижницький район
Chernivtsi Oblast / Чернівецька область Ukraine / Україна Google Maps
48.4659, 25.6572 Havrylivtsi / Гаврилівці Kitsman / Кіцманська
Chernivtsi Raion / Чернівецький район
Chernivtsi Oblast / Чернівецька область Ukraine / Україна Google Maps
Chernivtsi Raion / Чернівецький район
Chernivtsi Oblast / Чернівецька область Ukraine / Україна Google Maps
Chernivtsi Oblast / Чернівецька область
Ukraine / Україна Google Maps
48.3557, 25.645 Zeleniv / Зеленів Brusnytsia / Брусницька
Vyzhnytsia Raion / Вижницький район
Chernivtsi Oblast / Чернівецька область Ukraine / Україна Google Maps 48.5972, 25.439 Torhovytsia / Торговиця Horodenka / Городенківська
Kolomyia Raion / Коломийський район
Kolomyia Raion / Коломийський район
48.4742, 24.6989 Chorni Oslavy / Чорні Ослави Deliatyn / Делятинська
48.4706, 24.5732 Yaremche / Яремчанська
Nadvirna Raion / Надвірнянський район
Ivano-Frankivsk Oblast / Івано-Франківська область
Ukraine / Україна Google Maps
Ivano-Frankivsk Oblast / Івано-Франківська область Ukraine / Україна Google Maps 48.4555, 25.2264 Troitsia / Троїця Zabolotiv / Заболотівська
Ivano-Frankivsk Oblast / Івано-Франківська область
Nadvirna Raion / Надвірнянський район
Ivano-Frankivsk Oblast / Івано-Франківська область
Ukraine / Україна Google Maps
Ukraine / Україна Google Maps
253
Church of the Kyivan Patriarchate Standing Unknown 256
Church of the Holy Trinity 1868 Standing Hutsul
261
Church of St. Demetrius 1900 Standing Pokut 262
Church of Archangel Michael 1861 Standing Pokut 267
Church of the Holy Spirit Standing Transcarpathian 268
Church of the Presenta tion of Our Lord 1800* Burned Transcarpathian 270
Church of the Dormition of the Blessed Virgin Mary 1814 Standing Transcarpathian
271
Church of St. Elias Dismantled Transcarpathian
272
Church of the Presenta tion of the BVM 1804 Standing Transcarpathian 273
Church of the Dormition of the BVM Standing Boyko
48.4491, 24.5554
Yaremche / Яремчанська
48.3974, 24.5989 Mykulychyn / Микуличин Yaremche / Яремчанська
Nadvirna Raion / Надвірнянський район
Nadvirna Raion / Надвірнянський район
Ivano-Frankivsk Oblast / Івано-Франківська область
Ukraine / Україна Google Maps
Ivano-Frankivsk Oblast / Івано-Франківська область
Ukraine / Україна Google Maps
48.834, 24.8034 Markivtsi / Марківці
Tysmenytsia / Тисменицька
Ivano-Frankivsk Raion / ІваноФранківський район
Ivano-Frankivsk Oblast / Івано-Франківська область Ukraine / Україна Google Maps 48.9515, 24.7491 Vovchynets / Вовчинець
Ivano-Frankivsk / Івано-Франківська
Ivano-Frankivsk Raion / ІваноФранківський район
Ivano-Frankivsk Oblast / Івано-Франківська область Ukraine / Україна Google Maps 48.8236, 23.0585 Бистрий / Бистрий
Nyzhni Vorota / Нижньоворітська
Mukachevo Raion / Мукачівський район
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Mykhailo Sy rokhman 48.8236, 23.0585 Бистрий / Бистрий Nyzhni Vorota / Нижньоворітська
Mukachevo Raion / Мукачівський район
48.732, 22.9835
48.7374, 23.0859
Verkhnia Hrabivnyt sia / Верхня Грабівниця
Abranka / Абранка
Mukachevo Raion / Мукачівський район
Zhdeniievo / Жденіївська
Nyzhni Vorota / Нижньоворітська
Mukachevo Raion / Мукачівський район
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Mykhailo Sy rokhman
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Mykhailo Sy rokhman 48.8106, 23.0719 Tyshiv / Тишів Nyzhni Vorota / Нижньоворітська
Zakarpattia Oblast / Закарпатська область
Mukachevo Raion / Мукачівський район
48.7717, 22.9809
Zhdeniievo / Жденіївська
Mukachevo Raion / Мукачівський район
Zakarpattia Oblast / Закарпатська область
Ukraine / Україна Mykhailo Sy rokhman
Zakarpattia Oblast / Закарпатська область
Ukraine / Україна Mykhailo Sy rokhman
Ukraine / Україна Mykhailo Sy rokhman
276
Church of the Exaltation of the Holy Cross Standing Transcarpathian
279
Church of St. Nicholas Standing Hutsul
281
285
286
287
288
290
291
Church of St. Arch. Michael 1712 Standing Pokut
Church of the Cathedral Ave. The Virgin 1943 Standing Boyko
Church of the Nativity of the Blessed Virgin Standing Boyko
Church of the Assump tion 1903 Standing Hutsul
Church of the Interces sion The Virgin 1825 Standing Boyko
Church of the Descent of the Holy Spirit 1824 Standing Boyko
Church of the Exaltation of the Holy Cross 1732 Standing Opilska
48.7048, 23.1376 Volovets / Воловецька
49.0114, 24.3787
Mukachevo Raion / Мукачівський район
Kalush / Калуська Kalush Raion / Калуський район
Zakarpattia Oblast / Закарпатська область
Ukraine / Україна Mykhailo Sy rokhman
Ivano-Frankivsk Oblast / Івано-Франківська область Ukraine / Україна Google Maps
48.9052, 24.0014 Hrabiv / Грабів Dolyna / Долинська Kalush Raion / Калуський район
Ivano-Frankivsk Oblast / Івано-Франківська область Ukraine / Україна Google Maps 49.1006, 23.5863 Skole / Сколівська Stryi Raion / Стрийський район
Lviv Oblast / Львівська область Ukraine / Україна Google Maps 49.0964, 23.6164 Tyshivnytsia / Тишівниця Skole / Сколівська Stryi Raion / Стрийський район
Lviv Oblast / Львівська область Ukraine / Україна Google Maps 49.1089, 23.6377 Nyzhnie Syn ovydne / Нижнє Синьовидне Skole / Сколівська Stryi Raion / Стрийський район
Lviv Oblast / Львівська область Ukraine / Україна Google Maps 49.1605, 23.8712 Morshyn / Моршинська Stryi Raion / Стрийський район
Lviv Oblast / Львівська область Ukraine / Україна Google Maps
49.2552, 23.9217 Pidhirsti / Підгірці,
Stryi Raion / Стрийський район
Ivano-Frankivsk Raion / ІваноФранківський район
Ivano-Frankivsk Oblast / Івано-Франківська область Ukraine / Україна Google Maps
Lviv Oblast / Львівська область Ukraine / Україна Google Maps 49.769, 24.6338 Vyspa / Виспа Rohatyn / Рогатинська
295
Church of the Inter cession of the Blessed Virgin 1863 Standing Opilska 296
Church of St. John the Baptist 1742 Standing Opilska 297
Church of the Exaltation of the Holy Cross 1880 Standing Opilska 298
Church of St. George 1782 Standing Opilska 299
Wooden Church of the Exaltation of the Holy Cross Standing Opilska 301
Church of St. Nicholas Standing Transcarpathian 303
Church of Sts. Peter and Paul Standing Transcarpathian 304
Church of Holy Trinity Standing Transcarpathian 306
Church of St. Michael 1777 Standing Lemko 308
Church of the Presen tation of the Blessed Virgin Mary (BVM) 1808 Standing Transcarpathian
49.7193, 24.1738 Novosilky / Новосілки Zolochiv / Золочівська
49.7644, 24.244 Sholomyn / Шоломинь Davydiv / Давидівська
Zolochiv Raion / Золочівський район
Lviv Raion / Львівський район
Lviv Oblast / Львівська область Ukraine / Україна Google Maps
49.6928, 24.2711 Hlukhovychi / Глуховичі Pidberiztsi / Підберізцівська
49.6803, 24.1872 Kotsuriv / Коцурів Davydiv / Давидівська
Lviv Raion / Львівський район
Lviv Oblast / Львівська область Ukraine / Україна Google Maps
Lviv Raion / Львівський район
Lviv Oblast / Львівська область Ukraine / Україна Google Maps
Lviv Oblast / Львівська область Ukraine / Україна Google Maps
48.5335, 22.9781 Budkiv / Будьків Davydiv / Давидівська Lviv Raion / Львівський район
Mukachevo Raion / Мукачівський район
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Mykhailo Sy rokhman
Lviv Oblast / Львівська область Ukraine / Україна Google Maps 48.6689, 22.985 Svaliava / Свалявська
48.56, 22.7035 Uklyn / Уклин Poliana / Полянська Mukachevo Raion / Мукачівський район
48.6206, 22.3077 Ilkivtsi / Ільківці Ivanivtsi / Івановецька
48.6255, 23.357
Uzhhorod / Ужгородська
Mukachevo Raion / Мукачівський район
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Mykhailo Sy rokhman
Zakarpattia Oblast / Закарпатська область
Uzhhorod Raion / Ужгородський район
48.6493, 23.3673 Izky / Ізки Pylypets / Пилипецька
Khust Raion / Хустський район
Zakarpattia Oblast / Закарпатська область
Ukraine / Україна Mykhailo Sy rokhman
Zakarpattia Oblast / Закарпатська область
Ukraine / Україна Mykhailo Sy rokhman
Ukraine / Україна Mykhailo Sy rokhman
309
Church of St. Nicholas Standing Transcarpathian 313
Church of St. Basil the Great 1934 Standing Transcarpathian 315
Church of St. Nicholas 1804 Standing Boyko 316
Church of the Resurrec tion of Our Lord Standing Boyko 319
Church of St. Ignatius 1734 Standing Opilska 324
Church of St. Paraskeva 1822 Standing Opilska 325
Church of God’s Wis dom 1931 Standing Boyko 330
Church of the Interces sion The Virgin 1700* Standing Przemysl 332
Church of the Behead ing of St. John Baptist 1754 Standing Przemysl 334
Church of the Cathedral Ave. The Virgin 1864 Standing Przemysl
48.5664, 23.4778
Izky / Ізки
Pylypets / Пилипецька
48.7358, 23.3523
Mizhhiria / Міжгірська
48.5175, 23.5147
49.5957, 23.9091
Verkhnii Stude nyi / Верхній Студений
Khust Raion / Хустський район
Zakarpattia Oblast / Закарпатська область
Khust Raion / Хустський район
Pylypets / Пилипецька
Khust Raion / Хустський район
Zakarpattia Oblast / Закарпатська область
Ukraine / Україна
Mykhailo Sy rokhman
Ukraine / Україна
Mykhailo Sy rokhman
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Mykhailo Sy rokhman
Mizhhiria / Міжгірська Khust Raion / Хустський район
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Mykhailo Sy rokhman
49.8438, 24.0619
Horbachi / Горбачі
Schyrets / Щирецька Lviv Raion / Львівський район
Lviv Oblast / Львівська область Ukraine / Україна Google Maps 49.8438, 24.0659
Lviv / Львівська Lviv Raion / Львівський район
Lviv Oblast / Львівська область Ukraine / Україна Google Maps 49.3051, 23.3785 Lviv / Львівська Lviv Raion / Львівський район
Lviv Oblast / Львівська область Ukraine / Україна Google Maps 49.7827, 23.6425 Popeli / Попелі Boryslav / Бориславська
Drohobych Raion / Дрогобицький район
49.9411, 23.9087
Horodok / Городоцька Lviv Raion / Львівський район
Lviv Oblast / Львівська область Ukraine / Україна Google Maps
50.0553, 23.9821
Rokytne / Рокитне
Ivano-Frankove / Івано-Франківська
Yavoriv Raion / Яворівський район
Lviv Oblast / Львівська область Ukraine / Україна Google Maps
Lviv Oblast / Львівська область Ukraine / Україна Google Maps
335
Church of the Holy Trinity 1720 Standing Przemysl 336
Church of the Holy Apostles Peter and Paul 1820* Standing Przemysl 338
Church of the Assump tion of the Blessed Virgin 1739 Standing Hutsul 341
Church of the Cathedral of the Blessed Virgin 1659 Standing Przemysl 346
Church of the Interces sion The Virgin 1875 Standing Boyko 347
Church of the Cathedral of St. John the Baptist 1881 Standing Boyko 348
Church of the Cathedral of the Blessed Virgin 1895 Standing Boyko 350
Church of the Holy Virgin 1923 Standing Boyko 351
Church of the Descent of the Holy Spirit 1814 Standing Boyko
50.0397, 23.9498
Zhovkva / Жовківська
50.0522, 23.8104
Stara Skvariava / Стара Скварява
49.7964, 23.1954
Lelekhivka / Лелехівка
Lviv Raion / Львівський район
Zhovkva / Жовківська
Ivano-Frankove / Івано-Франківська
Lviv Raion / Львівський район
Lviv Oblast / Львівська область Ukraine / Україна Google Maps
Yavoriv Raion / Яворівський район
Lviv Oblast / Львівська область Ukraine / Україна Google Maps
Lviv Oblast / Львівська область Ukraine / Україна Google Maps
49.2805, 22.7603
Hostyntseve / Гостинцеве
Mostyska / Мостиська Yavoriv Raion / Яворівський район
Lviv Oblast / Львівська область Ukraine / Україна Google Maps 49.2546, 22.8302 Lopushanka / Лопушанка Turka / Турківська Sambir Raion / Самбірський район
Lviv Oblast / Львівська область Ukraine / Україна Google Maps 49.18, 22.9432 Limna / Лімна Turka / Турківська Sambir Raion / Самбірський район
Lviv Oblast / Львівська область Ukraine / Україна Google Maps 49.1167, 23.1309 Pryslip / Присліп Turka / Турківська Sambir Raion / Самбірський район
Lviv Oblast / Львівська область Ukraine / Україна Google Maps 49.0315, 23.0304 Radych / Радич Turka / Турківська Sambir Raion / Самбірський район
Lviv Oblast / Львівська область Ukraine / Україна Google Maps 48.9868, 23.1339
Nyzhnie Vys otske / Нижнє Висоцьке Borynia / Боринська Sambir Raion / Самбірський район
Lviv Oblast / Львівська область Ukraine / Україна Google Maps
352
Church of St. Demetrius 1889 Standing Boyko 353
Church of St. Nicholas 1655 Standing Boyko 356
Church of St. Basil 1703 Standing Transcarpathian 357
Church of St. Archangel Michael 1791 Standing Transcarpathian 358
Church of the Cathedral of St. John the Baptist 1879 Standing Boyko 360
Church of the Nativity of St. John the Baptist 1905 Standing Boyko 361
Church of the Holy Apostles Peter and Paul 1871 Standing Boyko 363
Church of the Exaltation of the Holy Cross 1844 Standing Boyko 365
Church of the Descent of the Holy Spirit 1795 Standing Transcarpathian 370
Church of the Nativity of the Virgin Standing Hutsul
48.9465, 22.8473 Zadilske / Задільське
Koziova / Козівська Stryi Raion / Стрийський район
48.9531, 22.5241 Husnyi / Гусний Stavne / Ставненська
48.8242, 22.6731 Sil / Сіль Kostryna / Костринська
Uzhhorod Raion / Ужгородський район
Lviv Oblast / Львівська область
Zakarpattia Oblast / Закарпатська область
Ukraine / Україна Google Maps
Ukraine / Україна Google Maps
Uzhhorod Raion / Ужгородський район
Zakarpattia Oblast / Закарпатська область
Ukraine / Україна Google Maps
48.9978, 23.2305 Bukivtsovo / Буківцьово
ДубриницькоМалоберезнянська / ДубриницькоМалоберезнянська
Uzhhorod Raion / Ужгородський район
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Google Maps 48.9011, 23.3185 Myta / Мита Koziova / Козівська Stryi Raion / Стрийський район
Lviv Oblast / Львівська область Ukraine / Україна Google Maps 49.1032, 23.2042 Plavia / Плав’я Koziova / Козівська Stryi Raion / Стрийський район
Lviv Oblast / Львівська область Ukraine / Україна Google Maps 48.7834, 23.3431 Zubrytsia / Зубриця Skhidnytsia / Східницька
Drohobych Raion / Дрогобицький район
48.4395, 23.6868
Oporets / Опорець
48.2841, 24.5703 Horb / Горб
Slavske / Славська
Stryi Raion / Стрийський район
Kolochava / Колочавська
48.1953, 24.7023
Vorokhta / Ворохтянська
Khust Raion / Хустський район
Lviv Oblast / Львівська область Ukraine / Україна Google Maps
Nadvirna Raion / Надвірнянський район
Lviv Oblast / Львівська область Ukraine / Україна Google Maps
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Google Maps
Ivano-Frankivsk Oblast / Івано-Франківська область Ukraine / Україна Google Maps
374
Church of The Prophet Elijah of the UGCC 1994 Standing Hutsul
376
Church of the Ascension 1927 Standing Hutsul 377
Church of St. Demetrius 1883 Standing Hutsul 380
Church of the Ascension 1938 Standing Hutsul 381
Church of the Holy Apostles Peter and Paul 1877 Standing Hutsul 385
Church of St. Archangel Michael Standing Lemko 386
Church of St. Nicholas 1700* Standing Lemko 387
Church of the Ascension of the Lord 1755 Standing Lemko
Church of St. John the Baptist 1800* Standing Lemko 389
388
Church of St. Archangel Michael 1718 Standing Lemko
48.259, 24.9847
Kryvopillia / Кривопілля
Verkhovyna / Верховинська
48.2412, 25.1551 Kosiv / Косівська
Verkhovyna Raion / Верховинський район
Kosiv Raion / Косівський район
Ivano-Frankivsk Oblast / Івано-Франківська область
Ukraine / Україна Google Maps
Ivano-Frankivsk Oblast / Івано-Франківська область
48.1218, 24.9946
Vyzhnytsia / Вижницька
Vyzhnytsia Raion / Вижницький район
Ukraine / Україна Google Maps
Chernivtsi Oblast / Чернівецька область Ukraine / Україна Google Maps
47.9047, 25.1412 Usteriky / Устеріки Biloberizka / Білоберізька
Verkhovyna Raion / Верховинський район
Vyzhnytsia Raion / Вижницький район
48.8568, 22.2966 Inovce / Inovce
Chernivtsi Oblast / Чернівецька область Ukraine / Україна Google Maps
Ivano-Frankivsk Oblast / Івано-Франківська область Ukraine / Україна Google Maps 48.8251, 22.3571 Ploska / Плоска Seliatyn / Селятинська
Region of Košice / Košický kraj Slovakia / Slov ensko Google Maps
Region of Košice / Košický kraj Slovakia / Slov ensko Google Maps 48.9745, 22.3091 Slovakia / Slov ensko Google Maps 48.9911, 22.4378 Kalná Roztoka / Kalná Roztoka Slovakia / Slov ensko Google Maps 49.0447, 22.3563 Uličské Krivé / Uličské Krivé Slovakia / Slov ensko Google Maps
48.8918, 22.3189 Ruská Bystrá / Ruská Bystrá
390
church of St. Archangel Michael 1700 Standing Lemko 391
Church of St. Archangel Michael 1740 Standing Lemko 392
Church Of St. George 1792 Standing Lemko 393
Church of St. Paraskieva 1773 Standing Lemko 395
Church of St. Michael 1777 Standing Lemko 396
Church of St. Michael the Archangel 1752 Standing Lemko 397
Church of Saint Mi chael the Archangel of Ladomirova 1742 Standing Lemko 398
Church of St. Paraskeva 1705 Standing Lemko 399
Church of St. Paraskevy of Nova Polianka 1766 Standing Lemko 400
Church of St. Luke the Evangelist 1500* Standing Lemko
49.0291, 22.4108 Topoľa / Topoľa Slovakia / Slov ensko Google Maps 49.0398, 22.2362 Ruský Potok / Ruský Potok Slovakia / Slov ensko Google Maps 49.2585, 21.6276 Slovakia / Slov ensko Google Maps 49.3624, 21.7387 Slovakia / Slov ensko Google Maps 49.3123, 21.6632
Region of Prešov / Prešovský kraj Slovakia / Slov ensko Google Maps 49.3283, 21.6265 Slovakia / Slov ensko Google Maps 49.3653, 21.6207 Ladomirová / Ladomirová Slovakia / Slov ensko Google Maps 49.3038, 21.5576 Slovakia / Slov ensko Google Maps 49.1817, 21.321
Region of Prešov / Prešovský kraj Slovakia / Slov ensko Google Maps 49.2168, 21.4885 Tročany / Troča ny Slovakia / Slov ensko Google Maps
401
Church of the Mother of God of Nicholas 1701 Standing Lemko
Church of the Encounter of the Lord with Simeon 1750* Standing Lemko 403
404
Church of the Presov. The Mother of God 1750* Standing Lemko 405
Church of St. Dimitri 1861 Standing Lemko 406
Church of St. Michael 1833 Standing Lemko 407
Church of Of the As sumption of the Blessed Virgin Mary 1791 Standing Boyko 408
410
Church of st. Nicholas 1589 Standing Boyko
Church of St. Nicholas 1756 Standing Boyko
411
Church of St. Dmitri 1800* Standing Lemko
Church of Mother of God 1700* Standing Boyko 412
49.3299, 21.2668
Region of Prešov / Prešovský kraj Slovakia / Slov ensko Google Maps
Slovakia / Slov ensko Google Maps 49.3983, 21.3728
49.3001, 20.937 Slovakia / Slov ensko Google Maps 49.3172, 20.6947 Leluchów / Le luchów gmina Muszyna / gmina Muszyna
Lesser Poland Voivode ship / województwo małopolskie Poland / Polska Google Maps 49.2096, 22.6878
Region of Prešov / Prešovský kraj Slovakia / Slov ensko Google Maps 49.2206, 22.6014 Smolnik / Smol nik gmina Lutowiska / gmina Lutowiska
Subcarpathian Voivode ship / województwo podkarpackie Poland / Polska Google Maps
Subcarpathian Voivode ship / województwo podkarpackie Poland / Polska Google Maps 49.4066, 22.5917 Rabe / Rabe gmina Czarna / gmina Czarna
49.3659, 22.661 Chmiel / Chmiel gmina Lutowiska / gmina Lutowiska
Subcarpathian Voivode ship / województwo podkarpackie Poland / Polska Google Maps
49.3042, 22.0477 Równia / Równia gmina Ustrzyki Dolne / gmina Us trzyki Dolne
Subcarpathian Voivode ship / województwo podkarpackie Poland / Polska Google Maps
Subcarpathian Voivode ship / województwo podkarpackie Poland / Polska Google Maps 49.3403, 22.0558 Radoszyce / Radoszyce gmina Komańcza / gmina Komańcza
413
Church of the Protec tion of the Mother of God 1802 Standing Lemko
414
Church of St. Michael the Archangel 1824 Standing Lemko
415
Church of St. Nicholas 1824 Standing Lemko
416
Church of the Dormition of the Mother of God 1888 Standing Lemko
417
Church of St. Paraskeva 1858 Standing Lemko
418
Church of St. Anne 1925 Standing Unknown
419
Church of st. Michael the Archangel 1843 Standing Lemko
420
Church of the Ascension 1659 Standing Przemysl
421
422
Church of the Nativity of the Blessed Virgin Mary 1731 Standing Przemysl
Church of St. Transfigu ration of the Lord 1742 Standing Przemysl
49.3691, 22.129 Komańcza / Komańcza gmina Komańcza / gmina Komańcza
49.3896, 22.1046 Turzańsk / Tur zańsk gmina Komańcza / gmina Komańcza
Subcarpathian Voivode ship / województwo podkarpackie Poland / Polska Google Maps
Subcarpathian Voivode ship / województwo podkarpackie Poland / Polska Google Maps
49.3941, 22.1332 Rzepedź / Rzepedź gmina Komańcza / gmina Komańcza
Subcarpathian Voivode ship / województwo podkarpackie Poland / Polska Google Maps
49.5823, 22.3299 Szczawne / Szcza wne gmina Komańcza / gmina Komańcza
Subcarpathian Voivode ship / województwo podkarpackie Poland / Polska Google Maps
Subcarpathian Voivode ship / województwo podkarpackie Poland / Polska Google Maps 49.7316, 22.4142 Hołuczków / Hołuczków gmina Tyrawa Wołoska / gmina Tyrawa Wołoska
49.6755, 22.3628 Stara Bircza / Stara Bircza gmina Bircza / gmi na Bircza
Subcarpathian Voivode ship / województwo podkarpackie Poland / Polska Google Maps 49.6756, 22.2782 Brzeżawa / Brzeżawa gmina Bircza / gmi na Bircza
Subcarpathian Voivode ship / województwo podkarpackie Poland / Polska Google Maps
49.5754, 22.2142 Ulucz / Ulucz gmina Dydnia / gmina Dydnia
49.5763, 22.1508
49.4176, 22.3411 Czerteż / Czerteż gmina Sanok / gmi na Sanok
Subcarpathian Voivode ship / województwo podkarpackie Poland / Polska Google Maps
Subcarpathian Voivode ship / województwo podkarpackie Poland / Polska Google Maps
Subcarpathian Voivode ship / województwo podkarpackie Poland / Polska Google Maps
423
Church of the Nativity of the Blessed Virgin Mary 1700* Standing Lemko 425
Church of St. Paraskeva 1933 Standing Hutsul 426
Church of Of the falling asleep of the Mother of God 1700* Standing Lemko 427
Church of St. Michael 1866 Standing Unknown 428
Church of St. Dimitri 1882 Standing Lemko 429
Church of St. Kosma and Damian 1782 Standing Lemko 430
Church of St. Michael the Archangel 1796 Standing Lemko 432
Church of St. Kosma and Damian 1700* Standing Lemko 434
Church of the Blessed Virgin Mary 1854 Standing Lemko 436
Church of Saints Cos mas and Damian 1842 Standing Lemko
49.4485, 21.7941 Średnia Wieś / Średnia Wieś gmina Lesko / gmina Lesko
Subcarpathian Voivode ship / województwo podkarpackie Poland / Polska Google Maps
49.5318, 21.802 Daliowa / Dalio wa gmina Jaśliska / gmina Jaśliska
Subcarpathian Voivode ship / województwo podkarpackie Poland / Polska Google Maps
49.7581, 22.2386 Bałucianka / Bałucianka gmina Rymanów / gmina Rymanów
Subcarpathian Voivode ship / województwo podkarpackie Poland / Polska Google Maps
Subcarpathian Voivode ship / województwo podkarpackie Poland / Polska Google Maps 49.5112, 21.5046 Siedliska / Sied liska gmina Nozdrzec / gmina Nozdrzec
49.7581, 22.2386 Siedliska / Sied liska gmina Nozdrzec / gmina Nozdrzec
Subcarpathian Voivode ship / województwo podkarpackie Poland / Polska Google Maps 49.5231, 21.4334 Krempna / Krempna gmina Krempna / gmina Krempna
Subcarpathian Voivode ship / województwo podkarpackie Poland / Polska Google Maps 49.5266, 21.4714 Świątkowa Wielka / Świątkowa Wielka gmina Krempna / gmina Krempna
Subcarpathian Voivode ship / województwo podkarpackie Poland / Polska Google Maps 49.5154, 21.3128 Kotań / Kotań gmina Krempna / gmina Krempna
Subcarpathian Voivode ship / województwo podkarpackie Poland / Polska Google Maps 49.5646, 21.3329 Krzywa / Krzywa gmina Sękowa / gmina Sękowa
Lesser Poland Voivode ship / województwo małopolskie Poland / Polska Google Maps 49.5902, 21.2933 Bartne / Bartne gmina Sękowa / gmina Sękowa
Lesser Poland Voivode ship / województwo małopolskie Poland / Polska Google Maps
437
Church of St. Demetrius 1932 Standing Lemko 438
Church of St. Cosmas and St. Damian 1807 Standing Lemko 439
Church of the Nativity of the Blessed Virgin Mary 1785 Standing Boyko 440
Church of St. Philip and St. James 1520 Standing Southern Malopolska Gothic 441
Church of St. Care of the Mother of God 1653 Standing Lemko 443
Church of the Nativity of the Blessed Virgin Mary 1790* Standing Lemko 444
Church of St. Luke the Apostle Standing Lemko 445
Church of St. Paraskeva 1843 Standing Lemko 446
Church of St. Michael the Archangel 1756 Standing Lemko 449
Church of Ascension of the Lord 1779 Standing Hutsul
49.6238, 21.2679 Bodaki / Bodaki gmina Sękowa / gmina Sękowa
Lesser Poland Voivode ship / województwo małopolskie Poland / Polska Google Maps
49.6313, 21.2839 Męcina Wielka / Męcina Wielka gmina Sękowa / gmina Sękowa
Lesser Poland Voivode ship / województwo małopolskie Poland / Polska Google Maps
49.6306, 21.186 Rozdziele / Rozdziele gmina Lipinki / gmina Lipinki
Lesser Poland Voivode ship / województwo małopolskie Poland / Polska Google Maps 49.5886, 21.1915 Sękowa / Sękowa gmina Sękowa / gmina Sękowa
Lesser Poland Voivode ship / województwo małopolskie Poland / Polska Google Maps 49.614, 21.0892 Owczary / Ow czary gmina Sękowa / gmina Sękowa
Lesser Poland Voivode ship / województwo małopolskie Poland / Polska Google Maps 49.5674, 21.1369 Szymbark / Szym bark gmina Gorlice / gmina Gorlice
Lesser Poland Voivode ship / województwo małopolskie Poland / Polska Google Maps 49.5535, 21.1809 Kunkowa / Kun kowa gmina Uście Gor lickie / gmina Uście Gorlickie
Lesser Poland Voivode ship / województwo małopolskie Poland / Polska Google Maps 49.5403, 21.2021 Nowica / Nowica gmina Uście Gor lickie / gmina Uście Gorlickie
Lesser Poland Voivode ship / województwo małopolskie Poland / Polska Google Maps
49.5178, 21.2616 Małastów / Małastów gmina Uście Gor lickie / gmina Uście Gorlickie
Lesser Poland Voivode ship / województwo małopolskie Poland / Polska Google Maps
Lesser Poland Voivode ship / województwo małopolskie Poland / Polska Google Maps 49.5532, 21.1275 Gładyszów / Gładyszów gmina Uście Gor lickie / gmina Uście Gorlickie
450
Church of St. Luke 1868 Standing Lemko
451
Church of St. Paraskeva 1700* Standing Lemko 452
Church of the Protec tion of the Mother of God 1871 Standing Lemko 454
Church of St. Kosma and Damian Standing Lemko 455
Church of Our Lady of the Rosary 1798 Standing Lemko 456
Church of st. Michael the Archangel 1797* Standing Lemko 457
Church of Our Lady of the Assumption 1800* Standing Lemko 458
Church of St. Antoni Padewsk 1858 Standing Lemko 459
Church of Our Lady of the Assumption 1830 Standing Lemko 460
Church of Our Lady of Perpetual Help 1842 Standing Lemko
49.5009, 21.1737 Kunkowa / Kun kowa gmina Uście Gor lickie / gmina Uście Gorlickie
49.4749, 21.1606 Kwiatoń / Kwi atoń
gmina Uście Gor lickie / gmina Uście Gorlickie
49.4679, 21.0494 Hańczowa / Hańczowa gmina Uście Gor lickie / gmina Uście Gorlickie
Lesser Poland Voivode ship / województwo małopolskie Poland / Polska Google Maps
Lesser Poland Voivode ship / województwo małopolskie Poland / Polska Google Maps
Lesser Poland Voivode ship / województwo małopolskie Poland / Polska Google Maps
49.492, 20.9958 Banica / Banica gmina Uście Gor lickie / gmina Uście Gorlickie
Lesser Poland Voivode ship / województwo małopolskie Poland / Polska Google Maps
Lesser Poland Voivode ship / województwo małopolskie Poland / Polska Google Maps 49.5191, 20.9892 Piorunka / Pio runka gmina Krynica-Zdrój / gmina Kryni ca-Zdrój
49.5341, 21.0327 Polany / Polany gmina Krynica-Zdrój / gmina Kryni ca-Zdrój
Lesser Poland Voivode ship / województwo małopolskie Poland / Polska Google Maps
Lesser Poland Voivode ship / województwo małopolskie Poland / Polska Google Maps 49.5675, 20.888 Brunary / Brunary gmina Uście Gor lickie / gmina Uście Gorlickie
49.5306, 20.8348 Ptaszkowa / Ptaszkowa gmina Grybów / gmina Grybów
Lesser Poland Voivode ship / województwo małopolskie Poland / Polska Google Maps
49.5025, 20.9712 Maciejowa / Maciejowa gmina Łabowa / gmina Łabowa
Lesser Poland Voivode ship / województwo małopolskie Poland / Polska Google Maps
49.4708, 20.9042 Berest / Berest gmina Krynica-Zdrój / gmina Kryni ca-Zdrój
Lesser Poland Voivode ship / województwo małopolskie Poland / Polska Google Maps
461
Church of St. Michael the Archangel 1826 Standing Lemko 463
Church of St. Kosma and Damian 1813 Standing Lemko 464
Church of the Blessed Virgin Mary of the Assumption 1864 Standing Lemko 465
Church of St. Michael the Archangel 1450* Standing Southern Malopolska Gothic 466
Church of Mother of God of the Rosary 1527 Standing Southern Malopolska Gothic 467
Church of St. John the Baptist 1500* Standing Southern Malopolska Gothic 470
Church of the Presenta tion of the BVM 1800* Standing Transcarpathian 471
Church of the Ascension of Our Lord 1936 Standing Transcarpathian 474
Church of St. John the Baptist Standing Transcarpathian 475
Church of St. Nicholas 1808 Burned Transcarpathian
49.3582, 20.8555 Łosie / Łosie gmina Łabowa / gmina Łabowa
49.3421, 20.822 Milik / Milik gmina Muszyna / gmina Muszyna
Lesser Poland Voivode ship / województwo małopolskie Poland / Polska Google Maps
Lesser Poland Voivode ship / województwo małopolskie Poland / Polska Google Maps
49.4664, 20.212 Andrzejówka / Andrzejówka gmina Muszyna / gmina Muszyna
Lesser Poland Voivode ship / województwo małopolskie Poland / Polska Google Maps 49.7342, 20.7743 Dębno / Dębno gmina Nowy Targ / gmina Nowy Targ
Lesser Poland Voivode ship / województwo małopolskie Poland / Polska Google Maps 49.7936, 21.0942 Podole-Górowa / Podole-Górowa gmina Gródek nad Dunajcem / gmina Gródek nad Duna jcem
Lesser Poland Voivode ship / województwo małopolskie Poland / Polska Google Maps 48.6036, 23.3663
Rzepiennik Suchy / Rzepiennik Suchy
gmina Rzepien nik Strzyżewski / gmina Rzepiennik Strzyżewski
Lesser Poland Voivode ship / województwo małopolskie Poland / Polska Google Maps 48.5999, 23.348 Potik / Потік Pylypets / Пилипецька
Khust Raion / Хустський район
Zakarpattia Oblast / Закарпатська область
Khust Raion / Хустський район
48.5692, 23.39 Titkivtsi / Тітківці Mizhhiria / Міжгірська
48.6637, 23.5687
Tiushka / Тюшка Pylypets / Пилипецька
Khust Raion / Хустський район
Zakarpattia Oblast / Закарпатська область
Ukraine / Україна Mykhailo Sy rokhman
Ukraine / Україна Mykhailo Sy rokhman 48.698, 23.5764 Potik / Потік Pylypets / Пилипецька
Zakarpattia Oblast / Закарпатська область
Khust Raion / Хустський район
Zakarpattia Oblast / Закарпатська область
Ukraine / Україна Mykhailo Sy rokhman
Ukraine / Україна Mykhailo Sy rokhman
476
Church of the Presenta tion of the BVM 1809 Standing Transcarpathian 477
Church of Nativity of the BVM Standing Boyko 480
Church of the Presenta tion of the BVM Standing Transcarpathian 486
Church of St. Nicholas 1770 Standing Transcarpathian 491
Church of St. Michael Burned Transcarpathian 493
Church of St. Nicholas 1800* Standing Transcarpathian 496
Church of St. Nicholas dismantled Transcarpathian 499
Church of the Ascension of Our Lord 1800* Burned Transcarpathian 500
Church of St. Nicholas 1760* Standing Transcarpathian 501
Church of St. Nicholas 1700* Standing Transcarpathian
48.5176, 23.5148 Torun / Торунь
48.7047, 23.2995
Mizhhiria / Міжгірська
Khust Raion / Хустський район
Mizhhiria / Міжгірська
Khust Raion / Хустський район
Zakarpattia Oblast / Закарпатська область
Ukraine / Україна
Zakarpattia Oblast / Закарпатська область
48.1186, 23.3978 Roztoka / Розтока Pylypets / Пилипецька Khust Raion / Хустський район
Ukraine / Україна
Mykhailo Sy rokhman
Mykhailo Sy rokhman
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Mykhailo Sy rokhman
48.1189, 23.7742
Sokyrnytsia / Сокирниця Khust / Хустська Khust Raion / Хустський район
Tiachiv Raion / Тячівський район
Zakarpattia Oblast / Закарпатська область
Ukraine / Україна Mykhailo Sy rokhman
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Mykhailo Sy rokhman 48.6148, 24.6603 Pidplesha / Підлеша Neresnytsia / Нересницька
48.0562, 23.5349
Verkhnii Maidan / Верхній Майдан
Nadvirna / Надвірнянська
Nadvirna Raion / Надвірнянський район
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Mykhailo Sy rokhman
Ivano-Frankivsk Oblast / Івано-Франківська область Ukraine / Україна Mykhailo Sy rokhman 48.0668, 24.0666 Ruske Pole / Руське Поле Tiachiv / Тячівська Tiachiv Raion / Тячівський район
47.9849, 23.9095 Velykyi Bychkiv / Великобичківська
47.9828, 23.9005 Dobrik / Добрік Solotvyno / Солотвинська
Rakhiv Raion / Рахівський район
Zakarpattia Oblast / Закарпатська область
Tiachiv Raion / Тячівський район
48.2697, 24.3822 Serednie Vodiane / Середнє Водяне Solotvyno / Солотвинська
Tiachiv Raion / Тячівський район
Zakarpattia Oblast / Закарпатська область
Ukraine / Україна Mykhailo Sy rokhman
Zakarpattia Oblast / Закарпатська область
Ukraine / Україна Mykhailo Sy rokhman
Ukraine / Україна Mykhailo Sy rokhman
503
Church of the Transfigu ration of Our Lord Standing Hutsul 504
Church of Christ the King Standing Hutsul 505
Church of the Myrrh Bearers Standing Transcarpathian 509
Church of the Protec tion of the BVM 1800* Standing Transcarpathian 512
Church of St. Basil the Great 1748 Standing Transcarpathian 513
Church of St. Michael Standing Transcarpathian 515
Church of the Ascension of Our Lord dismantled Transcarpathian 516
Church of Three Hier archs dismantled Transcarpathian
517
Church of the Protec tion of the BVM 1899 Standing Transcarpathian 518
Church of St. Arch. Michael 1700 Standing Boyko
48.0622, 24.4379
Yasinia / Ясінянська Rakhiv Raion / Рахівський район
Zakarpattia Oblast / Закарпатська область
48.3076, 24.3401 Luhy / Луги Bohdan / Богданська Rakhiv Raion / Рахівський район
48.3649, 22.9878 Chorna Tysa / Чорна Тиса Yasinia / Ясінянська Rakhiv Raion / Рахівський район
Zakarpattia Oblast / Закарпатська область
Ukraine / Україна Mykhailo Sy rokhman
Ukraine / Україна
Mykhailo Sy rokhman
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Mykhailo Sy rokhman
48.7771, 22.7225
Deshkovytsia / Дешковиця Irshava / Іршавська Khust Raion / Хустський район
Turi Remety / Тур’єРеметівська
48.0671, 24.2985
Tysobyken / Тисобикень
47.9921, 24.2018 Roztoky / Розтоки
Pyiterfolvo / Пийтерфолвівська
Uzhhorod Raion / Ужгородський район
Berehove Raion / Берегівський район
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Mykhailo Sy rokhman
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Mykhailo Sy rokhman 48.0747, 22.8804 Лікіцари / Лікіцари
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Mykhailo Sy rokhman
Bohdan / Богданська
48.0331, 23.6976
Kostylivka / Костилівка
48.951, 22.6846
Bilovartsi / Біловарці
Rakhiv / Рахівська
Rakhiv Raion / Рахівський район
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Mykhailo Sy rokhman
Rakhiv Raion / Рахівський район
Bedevlia / Бедевлянська
49.7123, 24.0728
Vyshka / Вишка
Kostryna / Костринська
Tiachiv Raion / Тячівський район
Zakarpattia Oblast / Закарпатська область
Ukraine / Україна Mykhailo Sy rokhman
Zakarpattia Oblast / Закарпатська область
Uzhhorod Raion / Ужгородський район
Zakarpattia Oblast / Закарпатська область
Ukraine / Україна Mykhailo Sy rokhman
Ukraine / Україна Maxim Ritus
519
Church of the Entry into the Church of Presb. The Virgin 1720 Standing Opilska
521
Church of St. Demetrius 1892 Standing Boyko
9
Church of St. Paraskev 1753 Standing Transcarpathian
11
Church of St. Nicholas 1797 Standing Transcarpathian
12
Church of the Virgin 1809 Standing Transcarpathian
49.1369, 23.105 Vovkiv / Вовків Solonka / Солонківська
49.1368, 23.1050 Losynets / Лосинець Turka / Турківська
Lviv Raion / Львівський район
Sambir Raion / Самбірський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
48.1359, 23.5103 Oleksandrivka / Олександрівка Khust / Хустська Khust Raion / Хустський район
Lviv Oblast / Львівська область Ukraine / Україна Maxim Ritus
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Maxim Ritus
48.7009, 23.5174 Pryslip / Присліп Mizhhiria / Міжгірська Khust Raion / Хустський район
48.6637, 23.5691 Torun / Торунь Mizhhiria / Міжгірська Khust Raion / Хустський район
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Maxim Ritus
Zakarpattia Oblast / Закарпатська область Ukraine / Україна Maxim Ritus
