Issuu

Surveying and modelling

jewish heritage sites - An ESJF Guide

Геодезичні дослідження та моделювання об’єктів єврейської спадщини

Surveying and modelling

jewish heritage sites - An ESJF Guide

Tetyana Kondratenko Denys Gorkovchuk

Surveying and modelling

jewish heritage sites - An ESJF Guide

With contributions from Diรกnรก Vonnรกk Alexandra Fishel Philip Carmel Editor Tetyana Kondratenko Sub-Editor Sean McLeod Corrector Victoria Zhuravskaya Literary editor Tatiana Nebesna

European Jewish Cemeteries Initiative Kaiserstraße 1, 60311, Frankfurt, Germany This document was produced in the framework of the pilot project "Pilot project — Protecting the Jewish cemeteries of Europe: A full mapping process with research and monitoring and individual costed proposals for protection (EAC/S10/2018)" carried out by the European Jewish Cemeteries Initiative, co-funded by the European Commission. First published 2020 © European Jewish Cemeteries Initiative, 2020 Some rights reserved.

This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

ISBN 978-3-9822085-6-5

Intro to the ESJF and Drone Technology

he ESJF European Jewish Cemeteries Initiative was set up in 2015 as an international non-profit public foundation registered in the Federal Republic of Germany with the core objective of protecting Jewish cemeteries in Central and Eastern Europe. Its mission has been to concentrate on those Jewish burial sites in the thousands of towns and villages where Jewish communities were wiped out in the Holocaust. Such cemeteries were destroyed, vandalised, and neglected over the course of some 80 years with no local Jewish communities to protect them. As of 2020, the ESJF has demarcated and fenced over 160 Jewish cemeteries across seven countries. The mission remains vast. It is estimated that there are over 8,000 Jewish burial sites in these regions. Our task is therefore to continue fencing but also to search for methodologies to protect the thousands of sites we cannot yet fence. More than anything else, this requires the support of local communities in the towns and villages of Central and Eastern Europe. It further requires the development of sustainable and cross-generational models by which local Jewish heritage becomes local historical heritage â€“ owned by all so the responsibility for its protection becomes a collective, partnered effort. As part of this project, supported by the European Commission in 2019-20, the ESJF physically surveyed some 1,500 Jewish cem9

Intro to the ESJF and Drone Technology

eteries in five European countries — Greece, Lithuania, Moldova, Slovakia, and Ukraine — utilising state-of-the-art drone technology and creating an online database reflecting the current — and often perilous — condition of these sites. This acts as a first point level of protection, mobilising local authorities and communities by providing them with physical evidence and direct contact with key parts of their local heritage, sometimes in settlements which were home to substantial and even majority Jewish populations prior to World War II. The use of drones in heritage protection fulfils two key elements of our mission. At the primary stage, it represents an unrivalled method for the collection of data, all the more so in areas of the former Soviet Union where definitive lists of Jewish cemeteries have never been compiled. After decades of neglect, many of these sites are surrounded by such dense vegetation that ground-level access to demarcate the boundaries is impossible. As a direct result of linking office-based historical research with drone surveys in the field, the ESJF has collated the physical existence of hundreds of previously unknown or unlisted sites, a massive contribution to the knowledge and preservation of Jewish and European heritage. Our initial goal was to utilise the drones for the collection of this data, but drone surveying is of course far more versatile than that. As an organisation with wide experience in the collection of data, we are highly cognisant of what the drones, developed with project-specific software and piloted by highly skilled operators, can contribute to the physical protection of these sites. By utilising the collected data to generate 3D designs for fencing projects, the ESJF has been able to create a unique repository of roll-out, costed projects, available to donors, local authorities, and national governments. This has enabled, probably for the first time, an understanding of the true scale of the mission at hand, as well as facilitating a planned approach to a long-term cemetery preservation. In short, data collection, preservation of the historical and physical record, management, and planning have been linked in 10

Surveying and modelling Jewish heritage sites. An ESJF guide

a pan-European project towards the protection of a pan-European heritage. In the coming years, as these drone surveys yield physical protection projects, the true value of this technology will come even more sharply into focus, not just on the physical level. We are acutely aware that it takes considerably more than walls to protect a cemetery. People protect cemeteries. The ESJF is committed through this project and also as a core principle of the organisation to engaging with young people to educate about Jewish history in these regions, to give a wider view of Jewish contributions to local history and heritage and to learn about values of a multicultural society based on tolerance and opposed to racism, xenophobia, and antisemitism. By bringing on board a whole new generation of local drone operators and young engineers and architects trained to utilise this data for physical preservation, we are creating a sustainable model which we hope will have much wider utilisation in the field of general heritage preservation in the years to come, and we look forward to sharing and developing this technology with our partners in the field. We are deeply grateful for the continued support of the European Commission for this work and for all those who have nurtured and developed this ground breaking project.

Philip Carmel Chief Executive Officer ESJF European Jewish Cemeteries Initiative

Surveying cemeteries with the help of UAVs: The ESJF Approach

nmanned aerial vehicles, or drones (their shorter and more commonly used name), are now widely used across a spectrum of industries and disciplines. I n the agro-industrial sector, for instance, companies use UAVs to monitor crop growth more closely and thereby increase their profit margins. T he construction industry was one of the first to capitalise on this technology. Photogrammetric programmes make it easy to determine the volume of displaced soil on construction sites, as well as accurately measure distances and height differentials. W hen disasters occur, they can help emergency responders promptly clarify the situation on the ground, making it easier to find the optimal solution. I n the oil and gas sector, their automatic detection tools make it possible to monitor pipelines for malfunctions (such as leaks), helping reduce the negative impact on the environment.

Surveying and modelling Jewish heritage sites. An ESJF guide

The above-mentioned industries provide only a few examples of the myriad applications of drone technology across numerous fields. This manual, however, will focus on the benefits of UAVs in studying cultural heritage sites. The ESJF European Jewish Cemeteries Initiative has years of experience in documenting and protecting Jewish Heritage in Central and Eastern Europe. In Ukraine alone, the foundation has erected protective fencing around more than 100 Jewish cemeteries. However, the real breakthrough achieved by the ESJF has been the development and implementation of a drone research system for Jewish cemeteries. In 2019, the ESJF, with the support of the European Commission, launched a pilot project entitled “Protecting Jewish Cemeteries: A full mapping process with research and monitoring and individual costed proposals for protection.” One of the principle goals of the project was to compile an open-access database listing the precise location of Jewish cemeteries around Europe. More than 1,500 cemeteries across five European countries needed to be mapped. The selection of the right methods and tools to do so was an integral factor in the timely delivery of these results. The idea to apply innovative technology came at the planning stage of the project, and proved to be highly productive. Photogrammetry is not a new science, but the rapid development of computer technology in recent years has allowed for the rapid generation of detailed, reliable topographic maps. This is integral to the study of Jewish cemeteries, laying the foundation for protective fencing projects. First of all, it should be noted that for this research, the organisation selected and successfully implemented 2 models of drone — the MAVIC 2 Pro (left) and the Phantom 4 RTK (right) — from DJI (Fig. 1). These models have very different technical characteristics and capabilities. The Phantom 4 RTK, for instance, has a built-in RTK (real time

Fig. 1

Дослідження кладовищ за допомогою БПЛА: досвід ESJF

kinematics) module, which allows its pilot to position the drone with an accuracy level of 1cm on its horizontal plane and 1.5 cm on its vertical plane, thereby producing more thorough measurements. The Mavic 2 Pro model does not have such a module; however, this did not prevent it from performing its tasks in full. The methodology of the team who carried out the project consists of two principal stages.

Fig. 2

The first stage takes place on the cemetery site itself. Researchers (grant staff) use the Drone Deploy application to programme a route for the UAV to fly around the site, incorporating all of the basic features and parameters of the area, as demonstrated in (Fig. 2). The drone operator then flies over the area taking photos of the object. This technology makes it possible to obtain information not only about the terrain, but also about any vegetation, buildings, and infrastructure on the site. The radical difference between photos taken from UAVs and those obtained with other devices (smartphone or camera) is the presence of a link to GPS coordinates, the so-called Geotag. These photos can be easily recognised not only by the size of the files (they are always very large), but also by their properties - for instance, the display of coordinates in the dialog box. The second stage is data processing and modeling.

Fig. 3

All photographs obtained by the researchers are passed on to engineers to create a digital terrain. Via photogrammetric processing of the data (photographs) obtained from the UAVs, the engineers are able to build a point cloud which fully and accurately reflects the state of the cemetery and the surrounding terrain (Fig. 3). These methods make it possible to comprehensively investigate the condition of the cemetery even without special engineering education (which is required, for example, to work with topographic maps). This 14

Surveying and modelling Jewish heritage sites. An ESJF guide

makes it possible to demonstrate the condition of the cemetery, as well as the threats posed by natural and urban factors. To counteract the effects of these factors, the ESJF is working to install fences around at-risk cemeteries. The point cloud can be easily integrated into architectural software systems. This allowed ESJF engineers to generate 3D models of fences which helped visualise the specifications of future construction projects. Moreover, this technology allows the user to quickly make adjustments to ready-made projects (Fig. 4). Thanks to the development and implementation of this technology, ESJF was able to more efficiently assess the condition of the surveyed cemeteries and better visualise future work. This optimised the process of designing 3D models and accelerated fence installation. We hope that the insights and experience conveyed in this guide will prove useful in the implementation of similar projects in future and that it will help preserve heritage on a broader scale. Alexander Bessarab, Director of the Ukrainian office of the ESJF European Jewish Cemeteries Initiative

Fig. 4

Content Drones for the preservation of cultural heritage (Chapter 1)...................................... 20 Introduction.............................................................................................................. 20 Choosing a drone solution......................................................................................... 23 1. Aircraft............................................................................................................ 24 1.1. Type of drone........................................................................................... 24 1.2. Accuracy (RTK/PPK technology)............................................................... 26 1.3. Drone characteristics overview................................................................ 28 2. Payloads......................................................................................................... 29 2.1. Camera.................................................................................................... 29 2.2. LiDAR....................................................................................................... 31 LiDAR Sensor Flight Altitude............................................................................ 31 Resolution....................................................................................................... 31 LiDAR Sensor Application................................................................................ 32 LiDAR VS Photogrammetry.............................................................................. 32 3. Overview of the best solutions......................................................................... 36 3.1. DJI Mavic 2 Pro........................................................................................ 36 3.2. DJI Phantom 4 RTK.................................................................................. 37 3.3. DJI Matrice 600+LiDAR........................................................................... 38 Further Reading........................................................................................................ 39 16

Processing of UAV Photogrammetric Survey Data (Chapter 2)................................... 40 Introduction.............................................................................................................. 40 1. Processing photogrammetric data in Agisoft Metashape.................................. 41 1.1. Interface of Agisoft Metashape Professional............................................. 41 1.2. Loading of data into a project and assessing quality................................ 42 1.3. Aligning photos........................................................................................ 44 1.4. Referencing photos. Use of markers......................................................... 48 1.5. Camera calibration.................................................................................. 50 1.6. Working with chunks................................................................................ 52 1.7. Building dense point cloud....................................................................... 53 1.8. Point cloud filtering.................................................................................. 56 1.9. Building polygonal model......................................................................... 57 1.10. Model texturing...................................................................................... 58 1.11. Building orthomosaic............................................................................. 59 1.12. Building a digital elevation model........................................................... 61 1.13. Data export............................................................................................ 64 2. Photogrammetric data processing in other software........................................ 64 2.1. Working with point clouds and 3D models in CloudCompare..................... 64 2.1.1. Working with large coordinates............................................................. 64 2.1.2. Data import and export......................................................................... 66 2.1.3. Point cloud filtering............................................................................... 67 17

2.1.4. Building elevation model....................................................................... 70 2.1.5. Point cloud rendering............................................................................ 72 2.2. Autodesk ReCap....................................................................................... 73 2.2.1. Point cloud data import into Autodesk ReCap........................................ 74 2.2.2. Data visualisation parameter settings................................................... 77 2.2.3. Point cloud cleaning.............................................................................. 77 2.2.4. Point cloud rendering............................................................................ 79 2.3. Autodesk AutoCAD Civil 3D...................................................................... 80 2.3.1. Working with a point cloud in AutoCAD Civil 3D..................................... 80 2.3.2. Building digital elevation model............................................................. 83 UAV data processing in Pix4D software in Phantom 4 RTK drone records (Chapter 3)........90 I. Creating new project.............................................................................................. 90 To create a new project:...................................................................................... 90 Importing images................................................................................................ 91 Selecting Output/Ground Control Points (GCP) Coordinate System....................... 92 Selecting processing options template................................................................. 93

II. Processing (Step 1).............................................................................................. 94 Processing parameters........................................................................................ 94 Adding ground control/checkpoints...................................................................... 96 Project optimisation........................................................................................... 100 III. Processing (Step 2)........................................................................................... 102 Working with point cloud.................................................................................... 103 IV. Processing (Step 3)............................................................................................ 106 Processing results............................................................................................. 108 Authors................................................................................................................... 110

Chapter 1

Drones for the preservation of cultural heritage Introduction

ultural heritage objects are sensitive to continuous atmospheric and anthropogenic attacks: their vulnerability means there is a continuous need to maintain and preserve them. While preservation helps to keep them in good condition and prevent further damage, it does involve intervention. Documenting heritage sites with up to date technology, especially producing 3D models based on photogrammetric modelling, helps in monitoring their condition: these models provide instant accurate information, which can help with future conservation work, settle legal debates, and aid in the planning of preservation measures (such as the fencing of Jewish cemeteries). Digital heritage projects are increasingly popular across the world. With their help it is possible to take virtual tours of faraway heritage sites, research them, and build communities that care about them. This sector is expanding, and offers careers in an interesting nexus between culture and the arts, architecture and engineering. In such projects, drones are undoubtedly one of the central tools. As such, understand20

Surveying and modelling Jewish heritage sites. An ESJF guide

ing their usage and potential is crucial for anyone hoping to work with heritage in the future. Drones are essential for: ocumenting large areas and capturing everything around obD jects of interest to properly understand the impacts of urbanisation, landscape, human activity, and a variety of other factors. Moreover, UAVs give their users an opportunity to quickly receive information about large territories and provide access to hard to reach areas which might not be visible from the ground level. This information can be used to calculate buffer zones and complete measurements between every point of the territory. ixing the current state of an object and receiving photos from F any angle, even of the highest points of a site. reating 3D surfaces/models of the heritage object which can C be used to gather comprehensive measurements, and put together maintenance and preservation programmes. Before starting a project, it is necessary to identify the type of data which will be used afterwards. This will help you choose the most suitable UAV for your work and avoid the expense of unnecessary add-ons. Drone data is generally used for the following purposes: Documentation (photos, 360 degree panoramas, videos) elative measurements (2D Orthomosaic, 3D Point Cloud, 3D R mesh etc.) bsolute measurements (2D Orthomosaic, 3D Point Cloud, 3D A mesh etc.) Documentation gives the user the opportunity to fully understand the context of their work. This might involve the use of photos, 360 degree 21

High relative accuracy Low absolute accuracy

Drones for the preservation of cultural heritage

Chapter 1

panoramas, and videos. Photo and video might be taken from close range to accurately portray the state of the material, whereas a 360 degree panorama provides an overview of a site from all directions. Relative measurements Ń an be used to plan a maintenance programme and attain comprehensive dimensions for an object. In this case all distances within the project (or the dimensions of the captured object) will be accurate, and can be used to calculate buffer zones and measure distances between objects. However, the absolute location might not fit the reality accurately, which is acceptable for some kinds of work but not others. Absolute measurements, unlike those described above, are data with an accurate geolocation in a defined coordinate system and can be included in a cadastre and other resources. There are several kinds of data which can be used for relative and absolute measurements: 2D ortho map in avGeoTiff or JPEG format. This can be used A to create a topographic map and digitise 2D contours digital terrain model (DTM) or digital surface model (DSM) reA flects elevation in the terrain 3D Point cloud or 3D mesh - gives a full representation of the A object within a 3 dimensional coordinate system Only after identifying the data which will be used for the maintenance programme of the cultural heritage object can you begin deciding which types of drone will be used for the project.

Surveying and modelling Jewish heritage sites. An ESJF guide

Choosing a drone solution Given almost any drone can be used to create photos or videos of the area of interest, this section will focus on choosing the best drone for gathering 2D and 3D mapping information. In terms of data acquisition it is worth noting that aircraft parameters will influence efficiency, while payload (camera, LiDAR, radar, etc.) will influence the data which can be used for further analysis. As such, if productivity needs to be increased, you must pay attention to aircraft specifications: flight time, weight, payload capacity, and construction. However, if specific information is needed, such as the ground level beneath trees, thermal data, or even just better photo resolution, your focus should be payload parameters. It is also possible to increase productivity by choosing a camera with better parameters, although this does not represent a distinction between aircraft, as they should all be able to carry it.

Drones for the preservation of cultural heritage

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1. Aircraft 1.1. Type of drone In this article you will be introduced to the four most widely used drone types: Multi-Rotor Fixed-Wing Single-Rotor Fixed-Wing Hybrid It is worth noting that, taking into account the speed with which drone technology is advancing, these types may have changed in the near future. To determine the right drone for your work, it is best to identify the type of objects which will be captured; whether you are looking at small, built plots or ancient cities spread over large territories will have a significant bearing on which drone is appropriate. This information will help decide which type of drone is most suitable in terms of the required space for take off or landing. Multi-Rotor Drones These drones are some of the smallest and lightest drones on the market. They have a limited distance, but they are the perfect flying vehicle for enthusiasts and aerial photographers. In terms of survey work these are the ideal instrument for beginner pilots, but are also favoured by more experienced users. These drones can usually spend around 30 minutes in the air carrying a lightweight payload, such as a camera. Some of them are even designed to accommodate heavy payloads like LiDAR sensors. The main advantages of these drones are: V ertical take off, which is essential in confined spaces or areas with multiple buildings. 24

Surveying and modelling Jewish heritage sites. An ESJF guide

Full camera inflight control (for those with integrated cameras). On top of mapping information, these drones allow users to gather photographs and videos from multiple angles. Fixed Wing Drones These drones are closer in design to airplanes, with wings providing the lift instead of rotors, making them very efficient. One of the main advantages of a fixed-wing UAV is that their simpler construction means they need far less maintenance and repair work, thus lowering the cost. They also have the ability to carry payloads for longer distances on less power. In some configurations this allows you to carry larger sensors, as well as having the ability to carry twin sensor configurations, allowing for a greater area coverage per flight. Most of these drones are capable of flying for 60-90 minutes running on an electric battery. However, some models can perform flights for hours using a fuel powered system or even solar energy. Unlike multi-rotor drones, there is no option to hover and a larger space for take off and landing is required. Single Rotor Helicopters These drones resemble miniature helicopters and can be gas or electric powered. Their single blade and ability to run on gas helps their stability and allows them to remain in the air over greater distances. These UAVs are usually used to transport heavier objects, including LiDAR systems. Given the expense of these drones compared to the previous two types, they are generally used for more specialised work. Fixed-Wing Hybrid This drone combines the cruising flight efficiency of fixed-wing aircrafts with the convenient vertical landing of multirotors. As such, it allows for flight in confined areas and lessens the risk of a hard landing. However,

Drones for the preservation of cultural heritage

Chapter 1

even these systems come with disadvantages. For instance, they are less stable than multi-rotor drones when hovering and have a lower wind-resistance. The comparison table below presents the advantages and disadvantages of different types of drones.

Multi-Rotor Pros

V TOL and hover flight Can operate in confined areas

Fixed-Wing

Single-Rotor

Fixed-Wing Hybrid

Long endurance

TOL and hover flight V

L ong endurance

Large area coverage

L ong endurance (with gas power)

L arge area coverage

Fast flight speed

Camera control

eavier payload capability H

TOL and long-enV durance flight

Easy to use Ability to carry heavy payloads (some models) Cons

Short flight time

Requires more training to fly

equires more training to fly R

No hovering Larger area for taking off and landing needed

ot perfect at either N hovering or forward flight ind-resistance W

Choosing the right aircraft will help you achieve maximum efficiency and reduce costs in the field.

1.2. Accuracy (RTK/PPK technology) Since it is vital to attain either a relatively accurate or absolutely accurate model, it is important to identify the parameters which will influence accuracy.

Surveying and modelling Jewish heritage sites. An ESJF guide

A relatively accurate model can be attained using only data from the drone (photos, lidar) without additional information from the droneâ€™s GNSS (Global Navigation Satellite System), or by only using GNSS data for faster processing. There are several ways to receive an absolutely accurate model: Using high precision on board GNSS Using Ground Control Points (GCP) Using a combination of both Some onboard GNSS systems allow their user to receive centimetre level accuracy from the antenna on the drone. This technology is usually called RTK (Real Time Kinematic) or PPK (Post Processing Kinematic). In this case, receiving absolute accuracy of the whole model can be managed using only drone data from GNSS and/or IMU and data from the camera or LiDAR without any additional information from the ground. The manner in which this GNSS data is integrated is also very important, as photogrammetry requires not just the Antenna coordinate, but also the image centre coordinate. In modern photogrammetry processing, the IMU is not very important, but for LiDAR processing, both IMU and GNSS data are essential. This integration of IMU and GNSS for LiDAR can be built in by the manufacturer; otherwise, you should plan for this integration in advance of your work. Another method for receiving an accurate model in photogrammetric processing is the use of GCPs. With these there is no need for high precision GNSS because the whole model will be assigned with marked points on the ground. However, opting for this method can increase the time spent on field work. The table below presents the advantages and disadvantages of different drone types and methods in obtaining accurate absolute measurements. 27

Drones for the preservation of cultural heritage

Chapter 1

GCPs+Drone without RTK/PPK

Drone without RTK

Drone with RTK

Drone with RTK+GCPs

Absolute accuracy

High

Low

High

Speed

Low

High

Low

Cost of field work

High

Low

High

It is also important to keep in mind that the accuracy of the whole project cannot be better than the accuracy of GCPs or the GNSS of the drone. Thus, GCPs should be measured by special equipment to provide the required accuracy. Using telephones with GPS geolocation is not sufficient. For facade reconstruction, a method is sometimes used in which GCPs on the walls are received with sub-centimetre level accuracy to increase the accuracy of the overall project. For control purposes, even when using RTK drones it is better to use Checkpoints instead of GCPs.

1.3. Drone characteristics overview There are many parameters which influence the choice of which UAV is best for heritage preservation projects. The table below will help you to identify which key features are most important for your project. Characteristics

Influence on

Flight time

Area which can be captured during one flight

Endurance (IP rating, magnetic interference resistance etc.)

Environment conditions

Take off type (vertical/horizontal)

Opportunity to operate in confined area

Positioning accuracy (PPK,RTK)

Speed and accuracy of field works

Maximum flight weight

Payload types (camera, LIDAR, etc)

Camera integration

Easy to use and processing options

Surveying and modelling Jewish heritage sites. An ESJF guide

2. Payloads As stated in section 1, the aircraft is only the instrument which carries the payload. In this section, the two most common payloads used in cultural heritage preservation will be described.

2.1. Camera The method for receiving 2D and 3D models from images is called Photogrammetry. Here you will be presented with the parameters which influence Photogrammetry processing, accuracy, and photo quality. The most important parameter which influences the level of detail of the object is Ground sampling distance (GSD). GSD is defined as the length (in inches, centimeters, or millimeters) between the centres of two consecutive pixels on your map. Alternatively, GSD can be thought of as the length of one pixel in your map. For example, if a drone achieves a GSD of 5cm/px, then we can understand that one pixel on this digital map corresponds to 5cm in reality. A map with a «smaller» GSD has higher resolution and more accuracy. When conducting photogrammetric missions with drones, the main factors that can affect your GSD are your camera quality (focal length and resolution), and flight altitude. For example, flying your drone at a higher altitude would allow you to cover more area in a shorter time, but would result in your data being captured at a lower resolution. Below is a breakdown of your choices when it comes to surveying and mapping with a drone. When choosing a camera you should pay attention to the following parameters: Sensor size is one of the key imaging parameters. Along with focal length, it is the core component in defining the ground sample distance (GSD) – the pixel size in the real world – of a survey configuration. Larger 29

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sensors provide a wider imaging swath and therefore require fewer passes over an area to conduct an aerial survey. This translates to less time in the air, less fuel consumed, and lower overall operating costs. Pixel pitch – the width of each pixel on the sensor. This is intimately linked with the aperture – the size of the opening allowing light through the lens – as it will determine how big an effect diffraction will have on each image. Thus, while GSD might remain constant for two imaging configurations, one might be diffraction-limited and therefore perform significantly worse in practice. To mitigate these effects, a higher pixel pitch is desirable. Shutter type. Global and rolling shutters have different ways of exposing the camera sensor to light in order to register an image. A global shutter exposes all of the pixels of the sensor at once, whereas a rolling shutter exposes each pixel row of the sensor sequentially. Dynamic range expresses the range of brightness levels that the camera can extract detail from in a single image As you can see, there is no mention of “megapixels,” only the size of each pixel.

Surveying and modelling Jewish heritage sites. An ESJF guide

2.2. LiDAR LiDARâ€“ Light Detection and Ranging or LiDAR is a remote sensing technology by which the environment is scanned with a pulsed laser beam and the reflection time of the signal from the object back to the detector is measured. Terrestrial and airborne LiDAR have been a boon to archaeologists and historians. One reason for this is that LiDAR can penetrate vegetation. This allows for structures behind and below obscuring canopies or densely planted agriculture to be identified and measured. LiDAR Sensor Flight Altitude In LiDAR mapping, the flight altitude is a key parameter in picking the appropriate sensor. If you can fly below 60m AGL, tactical-range sensors are appropriate. For altitudes higher than 60m, you must consider either mid-range or long-range LiDAR sensors, as shown below. Resolution The projection of a single laser beam onto the ground yields its resolution. The resolution is a function of the flight altitude, scan rate (frequency), and the angular resolution. A minimum resolution for the tactical/ mid-range LiDAR is about 5-10cm at an altitude of 30m; while for longrange LiDARs it is about 2-3cm at an altitude of 100m. The frequency and angular resolution of the tactical-range and mid-range LiDAR sensors are in the same range. Thus, if you need a similar or higher resolution at higher altitudes, you must use a long-range LIDAR sensor.

Drones for the preservation of cultural heritage

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LiDAR Sensor Application The last parameter to be considered is the application. For applications in archaeology, where canopy penetration and ground returns are required, the number of channels and returns provided by the sensor will help narrow your selection. For example Velodyne LiDAR sensors provide up to two returns, while the Quanergy M8 LiDAR provides up to three returns, and the Teledyne-Optech LiDAR sensors provides up to four returns. LiDAR VS Photogrammetry LiDAR and photogrammetry make sense as tools for archaeologists who need to parse the structures and layout of an ancient site. LiDAR output takes less capture and process time. It also tends to produce a sharper point cloud thatâ€™s easier to work with.

Maximum flight altitude

AGL: 100m-500m (frequency dependent) AGL: < 120 m

speed < 5 m/s Tactical range LiDAR Velodyne VLP-16 Velodyne HDL-32E Quanergy M8-Plus

speed < 10 m/s Mid range LiDAR Velodyne PUCK 32MR Velodyne VLP-32E Quanergy M8-Ultra

-600 ge: 1 60 Effec tive r an

Effe

Ef f ect ive ra

ctive

nge : 75

rang e: 2 00 m

AGL: < 60 m

speed < 50 m/s Long range LiDAR Teledyne Optech CL-90 Teledyne Optech CL-360

Surveying and modelling Jewish heritage sites. An ESJF guide

LiDAR benefits Multiple returns AGL

Fast processing Live visualization

Vertical resolution Angle 2*, 1.25*

Horizontalresolution Angle 0,1* - 0.4*

Information on every point Intensity Direct georeferencing Reconstructs complex and irregular objects, e.g. vegetation & powerlines

Footprint diametr (across)

Footprint diametr (along)

Drones for the preservation of cultural heritage

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LiDAR drawbacks H arder interpretation H eavier payloads R eputation for difficulty Photogrammetry benefits D enser point clouds E asy to use, easy to process I ntuitive N atural RGB information L ightweight, small carrier Photogrammetry drawbacks C omputing power requirements S ensitive to lighting (passive sensor) R equires narrow flight lines to ensure sufficient lateral image overlap N eeds rich texture in images â€“ homogeneous surfaces (featureless/monochromatic color surfaces) canâ€™t be 3D reconstructed with high precision When it comes to LiDAR vs photogrammetry, both solutions have unique capabilities.

Surveying and modelling Jewish heritage sites. An ESJF guide

Combining LiDAR and Photogrammetry Add RGB information to enrich the LiDAR data Photo interpretation in laser point cloud helps avoid mistakes Dual collection in one flight, optimal way of gathering data

Drones for the preservation of cultural heritage

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3. Overview of the best solutions For cultural heritage work, multi-rotor drones are generally the best option, since they are easy to use and can be employed in restricted conditions like built territory. The majority of archeological sites are not too expansive, meaning there is no need to use fixed-wing drones. Moreover, it is possible to carry out complicated work by hovering at certain points to achieve better photo/video resolution.

3.1. DJI Mavic 2 Pro Benefits: Compact Affordable Easy to use Camera integration Camera quality The feature that makes this drone stand out from its competition is that it allows the gadget to have a massive ‘active sensing area’. The Mavic 2 Pro has an integrated camera with a 1-inch CMOS Sensor. It is able to capture more information in every pixel and create a more dynamic range of pictures by reducing noise and enhancing low-light performance. The drone has a 10-bit Dlog-M colour profile and 10-bit HDR Video with 4K 10-bit HDR support that comes with ‘immediate playback support’. Aside from using this drone for documentation purposes it also can be used to generate accurate relative models. Using GCPs, it is also possible to receive high absolute accuracy. 36

Surveying and modelling Jewish heritage sites. An ESJF guide

3.2. DJI Phantom 4 RTK Benefits: Easy to use Camera parameters Mechanical shutter RTK/PPK on board This drone is used for survey-grade operations. Its 1-inch, 20-megapixel CMOS sensor allows it to receive high quality image data. Due to the high resolution, the Phantom 4 RTK can achieve a ground sample distance (GSD) of 2.74cm at a flight altitude of 100 metres. The mechanical shutter makes mapping missions or regular data capture seamless as the Phantom 4 RTK can move while taking pictures without the risk of rolling shutter blur. To ensure each Phantom 4 RTK offers unparalleled accuracy, every single camera lens goes through a rigorous calibration process, with parameters saved into each imageâ€™s metadata, letting post-processing software adjust uniquely for every user. Its RTK module is integrated directly into the Phantom 4 RTK, providing real-time, centimetre-level positioning data for improved absolute accuracy on image metadata. In addition to optimized flight safety and precise data collection, the Phantom 4 RTK stores satellite observation data to be used for Post Processed Kinematics (PPK). These parameters allow the user to instantly receive accurate 2D and 3D information with high absolute accuracy.

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3.3. DJI Matrice 600+LiDAR Benefits: M ultifunctional H eavy lifter D ifferent sensors can be integrated (camera and LiDAR) The vast majority of LiDARs can be integrated with M600, so it is the best solution for carrying most heavy payloads. This UAV can sustain a flight duration of 15-20 minutes, dependent on environmental factors. Data can be collected from an area of at least 400 x 400m in a single flight. Equipped with additional batteries, it is possible to capture more than 200ha per day. This manual presents drone solutions manufactured by DJI because it is the industry leader and provides reliable UAVs. However, it is quite difficult to highlight the best LiDAR providers as almost every solution has some unique feature. For archeological surveys, tactical range LiDARs are generally the most suitable. LiDARs like Velodyne HDL 32 or VLP 16 would be sufficient for the majority of projects. However, it is necessary to pay attention to LiDAR integration and provide high precision GNSS and IMU integration. As such, solutions like Yellowscan and Phoenix etc. might be most apt in receiving comprehensive survey data. Payloads with both camera and LiDAR integration can also be integrated on the DJI Matrice 600 to receive all necessary data in the course of a single flight.

Surveying and modelling Jewish heritage sites. An ESJF guide

Further Reading 1.

https://www.architecturelab.net/best-drones-for-architectural-photography/

https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W9/45/2019/isprsarchives-XLII-2-W9-45-2019.pdf

https://www.yellowscan-lidar.com/knowledge/urban-myth-lidar-and-photogrammetry-areredundant/

https://www.goldensoftware.com/blog/industry-application-lidar-best-practices-for-culturalheritage-and-archaeology-practitioners

https://www.directionsmag.com/article/6807

https://www.dronezon.com/learn-about-drones-quadcopters/best-lidar-sensors-for-dronesgreat-uses-for-lidar-sensors/

https://geodetics.com/lidar-sensors-for-your-drone/

https://www.routescene.com/case-studies/routescene-use-drone-based-lidar-system-touncover-700-year-old-structures/

https://www.pix4d.com/blog/accuracy-aerial-mapping

10. https://wingtra.com/how-ground-sample-distance-gsd-relates-to-accuracy-and-drone-roi/

Chapter 2

Processing of UAV Photogrammetric Survey Data Introduction

erial photogrammetric surveys have been used to create topographic maps since the 19th century, almost since the invention of cameras. Photogrammetry is a science that studies the phenomena, shapes, and positions of various objects in space, determining objects and their dimensions by measuring their photographic image. By processing photographic materials, one can calculate the three-dimensional coordinates of points of objects and thus create their 3D models. The current state of the art in the field of unmanned aerial vehicles (UAVs) and digital technology makes aerial photogrammetric surveying much more accessible than 10 years ago. The accuracy and detail of photogrammetric models has improved significantly over recent years and is currently not inferior to classical instrumental methods. That is why UAV photogrammetric surveying is today one of the most popular methods for large-scale mapping and the creation of three-dimensional terrain models. Here are some guidelines for processing photogrammetric data in Agisoft Metashape and further processing the obtained results in CloudCompare, Autodesk ReCap, and Civil 3D. 40

Surveying and modelling Jewish heritage sites. An ESJF guide

1. P rocessing photogrammetric data in Agisoft Metashape 1.1. Interface of Agisoft Metashape Professional Agisoft Metashape allows for the photogrammetric processing of digital images (aerial and close-range photography, satellite imagery) and generation of spatial data based on the images in the form of point clouds, textured polygonal models, digital elevation models, and orthophotos. The software interface consists of the following main elements (Fig. 1): 1. 2. 3. 3.1. 3.2. 4.

Menu bar Toolbar Project pane with tabs Workspace Reference Model space with tabs

4.1. 4.2. 5. 6. 7.

Model Ortho Console Jobs Photos

3 4.1 4.2

Fig. 1 Agisoft Metashape main window interface

3.1 3.2 41

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Depending on the user needs, the panes can be moved within the active window or closed. If a closed pane needs to be reopened in the course of work, it can be done in the View menu by choosing the corresponding pane name (Fig. 2). Typically, photogrammetry processing consists of the following main steps: 1) Loading of data into a project 2) Aligning photos 3) Referencing photos 4) Camera calibration Fig. 2 Configuration of active panes in the View menu

5) Building dense point cloud 6) Building polygonal model 7) Generating orthophotos

1.2. Loading of data into a project and assessing quality Agisoft Metashape works with most popular bitmap formats, such as JPEG, PNG, BMP, TIFF, and TGA, among others. If the surveying system generates images in an unsupported format (e.g., RAW, NEF), such images must be converted to one of the supported formats using the camera manufacturerâ€™s software or universal converters. ! When preparing data for loading into the software, any geometric processing of photos (resizing, cropping, rotating, distortion correction, etc.) is not allowed. Such actions will make it impossible to build a correct camera model.

Fig. 3 Loading of images

Use the Add Photos command from the Workflow menu to load images into a project or select Add Folder command from the Workflow menu if you want to add all images contained in one folder (Fig. 3); 42

Surveying and modelling Jewish heritage sites. An ESJF guide

The photos loaded into a project will automatically appear in the Cameras folder in the project tree on the Workspace tab and on the Photos pane. If a project uses images with known coordinates of photography centres, their spatial position will immediately be displayed as blue balls on the Model pane (Fig. 4).

Fig. 4 Visualisation of loaded images

In this case, the NA (Not Aligned) mark will appear in the project tree next to the image file names, which indicates that the images are not aligned. If the NC (No Calibration) mark appears next to a photo, it means that no EXIF data with a description of camera characteristics is available for the image, which makes it difficult to build a correct camera model. If such a mark appears, you should check whether the images have been preprocessed in graphic editors. If a project contains poor quality (defocused, blurred, etc.) images, then using them will only increase the project processing time and make the results less reliable. Metashape allows you to estimate the 43

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quality of loaded images and, if necessary, disable unwanted images. To do this, go to the detailed view in the Photos pane (Fig. 5).

Fig. 5 Switching to the detailed view of photos

The images will be displayed as a table with detailed characteristics. To evaluate the quality of the images, right click any image and select the Estimate Image Quality command from the context menu. Select All Images in the next window to estimate all project photos. Once the analysis procedure is over, a figure (from 0 to 1) indicating estimated image quality will be displayed in the table with images in the Quality column: 0 â€“ poor quality image; 1 â€“ good quality image (Fig. 6). By clicking on the Quality header in the table, you can sort images by quality to view the worst images. Double-clicking on the image name on the Photos pane or in the project resource tree will open the original image in a separate tab next to Model. It is necessary next to visually assess the image quality and, if it is really unsatisfactory, it is better to disable such an image by right-clicking on the image name and selecting Disable Cameras from the menu.

1.3. Aligning photos Fig. 6 Image quality estimation results

The following functions are performed when photos are aligned: S earching for characteristic points in photos. Searching for matches between these points. etermining the relative position of the image planes and comD mon points, and at the same time, determining the characteristics of the optical system that are most appropriate to the defined parameters. Building a sparse point cloud model. To perform this procedure, select the Align Photos command from the Workflow menu. The following parameters control the photo alignment procedure (Fig. 7): 44

Surveying and modelling Jewish heritage sites. An ESJF guide

In the Generic preselection mode the pairs are selected by matching photos using a lower accuracy setting. In the Reference preselection mode the pairs are selected based on measured camera locations, if any: S ource – preselection based on known photography centres (recommended if images are obtained from a Accuracy This parameter determines the size of the images that the software will use to search for matches. At High accuracy setting the software works with photos of the original size. Medium setting causes image downscaling by a factor of 4 (2 times on each side). At Low accuracy images are downscaled by factor of 16, etc. The Very High accuracy setting upscales the image by factor of 4. Higher parameter settings help in obtaining more accurate camera position estimates. It should be noted, however, that the higher the accuracy setting, the longer it will take to obtain estimates. Due to the time it takes to process, the Very High accuracy setting is recommended only for very sharp images, mostly for research purposes. Pair preselection The image pair preselection option helps speed up the process of aligning large photo sets by preselecting a subset of image pairs to be matched. UAV (using GPS). E stimated – preselection based on the calculated values of the external orientation of the camera (recommended if you have a project with an alignment already performed, but you need to run it again with new parameters). S equential – the pairs are selected according to the sequence numbers of images, taking into account the fact sequential images overlap.

Fig. 7 Photo alignment parameters

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! Reference preselection is not recommended in cases where there are gross errors in the position of photography centres (for example, when a survey was performed in the absence of a GPS signal).

Reset current alignment This parameter is available for realigning photos. If this option is checked, all the data on the defined feature points will be deleted and the alignment procedure will be started from the very beginning. Point limit Key point limit and Tie point limit parameters indicate the upper limit of feature points and matching points to be taken into account during the current processing stage. The default values (40,000 and 4,000) are suitable for most cases. Using the zero value of these parameters turns off point filters, but it may result in a high number of less reliable points. Apply masks If this option is selected, feature points or matching points found in the masked area on a photo are excluded. Guided image matching This option allows for the discovery of additional feature points for high-resolution images. It is recommended for image sizes above 50 megapixels. Adaptive camera model fitting This option enables the automatic selection of camera parameters based on their reliability estimates. For data sets with strong camera geometry, it helps adjust more parameters during initial camera alignment, while for data sets with weak camera geometry, it helps prevent significant divergence of some camera parameters. It is advisable to 46

Surveying and modelling Jewish heritage sites. An ESJF guide

Fig. 8 Photo alignment results

always enable this option, except when using a pre-calibrated camera with known parameters. When the image alignment procedure is complete, the following data is generated: F sparse point terrain model consisting of common points. T he estimated coordinates and parameters of image orientation. T he refined (roughly) camera calibration parameters. The project resource tree will specify the number of images that have been successfully aligned, and the calculated camera position coordinates will be displayed on the Model pane (Fig. 8). If any images are not aligned (NA is left next to the image), you must repeat the alignment with other parameters. If certain images still do not align when realigned, they can be linked manually using markers (see Sec. 1.4). 47

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1.4. Referencing photos. Use of markers Referencing photos allows users to: 1) Orient the project in a certain geodetic coordinate system. Fig. 9 Loading of reference points

2) Manually align images that have not been aligned automatically. With high requirements for the accuracy of photogrammetric materials, it is necessary to perform geodetic referencing. There are two basic methods: 1) Use of UAVs with a high-precision RTK GPS receiver. 2) Coordination of reference points by classical geodetic methods (survey using GPS or total stations). In the first case, the images loaded into the project will already have highly accurate coordinates for projection centres (centimeter range for RTK GPS); accordingly, the results will have the same accuracy. In the second case, in order to reference the images, you need to load the coordinates of the reference points into the project and recognise them on the images. To load reference points into the project, click the Import Reference button on the Reference pane (Fig. 9).

Fig. 10 Reference point coordinate import settings

Next, you need to select the text file that contains the coordinates of the reference points and, in the import window, specify the correct coordinate system, the value separator in the text file, and the column order (Fig. 10). A preview of the parsing of the text file will be displayed at the bottom of the window. Make sure that all values are correctly distributed in the columns.

Fig. 11 Reference points loaded into the project

In the next window, the software will ask whether to create a new marker for the imported point. Select “Yes to all” to create markers for all reference points. In Metashape, “markers” are understood both as reference points that serve to reference images in the geodetic coordinate system and as additional points that users can create themselves to improve alignment quality. 48

Surveying and modelling Jewish heritage sites. An ESJF guide

All created markers will appear on the Reference pane (Fig. 11). After that it is necessary to place markers on the images. To do so, open the image by double-clicking the image file name on the Photos pane or in the project resource tree. You need to find the reference point location on the image itself, right-click on it, select Place Marker, and select the name of the marker from the dropdown list (Fig. 12). A green flag marker will then appear in the image. This procedure must be performed for all reference points in all of the images in which these reference points are visible. After placing markers on at least two aligned images, the software will automatically calculate the approximate position of these markers on other images and mark them with grey flags. This position is easy to adjust by moving the flag to the correct place, after which the flag will turn green.

Fig. 12 Placement of markers on images

After placing the reference points, click the Update Transform button on the Reference pane (Fig. 13). Metashape will recalculate the camera positions based on the reference point coordinates. The error values of the position on the images Error (pix) and the terrain Error (m) will appear in the marker table (Fig. 14).

Fig. 13 Reference information update

Fig. 14 Reference points with calculated error values

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Referencing is considered satisfactory if the error values of the marker position on the image Error (pix) do not exceed 0.5 pixels and the error values of the position on the terrain Error (m) lie within the accuracy range of geodetic methods used to determine the reference point coordinates. If the error values of the marker position on the image Error (pix) exceed 1 pixel, this may indicate poor camera calibration quality (see Sec. 1.5). You can also use markers to manually reference photos that have not been aligned automatically. For this purpose, place at least 4 markers on the unaligned image, each of which is placed on at least two aligned images. Such markers can be both reference points and additional markers created by the user. You can create a new marker by right-clicking on the marker table and selecting Add Marker. ! You can create a new marker not only in the marker table but also directly on the image by selecting Add Marker from the right-click context menu. However, in this case, the software will automatically calculate the coordinates of this marker from the current alignment and will use them as reference coordinates. If the camera calibration is of poor quality, these coordinates will be inaccurate and the marker position in some images will deviate from the correct one. Therefore, this method of creating markers should be used after calibrating the camera

After placing the markers, select the unaligned images in the Photos pane and select Align Selected Cameras from the right-click context menu.

1.5. Camera calibration Metashape calibrates the camera automatically based on feature points in the images recognised during the alignment stage. The automatic calibration result can be estimated using the error value of the position 50

Surveying and modelling Jewish heritage sites. An ESJF guide

Fig. 15 Updated error values of marker positions after optimising cameras

on the image Error (pix) in the marker table. If the project does not use reference points, then you should manually create 3-7 markers and place them on the feature points of the terrain that are uniquely identified in order to check the calibration. For a stable camera, the Error (pix) value should be about 0.5 pixels. In the case of higher values, camera calibration parameters should be adjusted. For this purpose, after placing all markers, it is necessary to execute the Optimise Cameras command from the Tools menu. The software will automatically mark the parameters to be adjusted in the pop-up window, so you just need to click OK. After the procedure is complete, the software will calculate the adjusted camera calibration parameters and refine the position of the images. The error values will be updated in the marker table (Fig. 15). If the error values have not decreased after the camera optimisation, this may indicate: I naccurate placement of markers on photos (check the quality of placing markers). C amera instability (disable the systemâ€™s camera settings that affect the cameraâ€™s optical settings, such as autofocus, before taking photos). 51

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! To ensure correct calibration, markers should be placed on the maximum number of images and, if possible, in different parts of the frame (both in the centre and at the edges). If you put markers on only some of the images, the camera model will fit exactly to these images, but will become inadequate for the rest of the images. In this case, the errors in the marker table will have minimal values, which is not true.

Fig. 16 Creation of a new chunk

! When the camera optimisation procedure is completed, all dense point clouds and 3D models, if any, will be deleted from the project. Since building a dense point cloud and a 3D model are the most time-consuming stages of photogrammetric data processing, it is highly recommended to perform camera optimisation before building a dense point cloud.

1.6. Working with chunks Metashape allows users to process a project in parts (separate chunks) and then put the chunks together into a single model. This is recommended in the following situations: 1) The project is too large to process all photographs in one go; 2) All photographs do not align automatically, while their separate groups can be aligned with each other. To create a new chunk, click on the Add Chunk button in the project resource tree (Fig. 16). Metashape only works with one chunk at a time, so you must double-click a chunk name in the resource tree to activate a chunk. The data is then processed as per the standard procedure. In order to move images from one chunk to another (for example, in a situation in which all images in one chunk do not align), you must select these images in the resource tree, right-click on them, and choose Move Cameras â€“ Chunks â€“ Chunk name command from the context menu. 52

Surveying and modelling Jewish heritage sites. An ESJF guide

When all photos in each chunk are aligned, it is necessary to align the chunks with each other. To do this, select the Align Chunks command from the Workflow menu. A window will open in which you need to mark the chunks you want to align and the method of alignment: The Point based method uses feature points similarly to aligning separate images with each other. It is advisable if such feature points are available. If a project is split into chunks because images in one chunk do not align automatically, it indicates that the images have not enough feature points and that this chunk alignment method will be less effective. The Marker based method uses markers. When this method is used, the chunks must have at least 4 common markers. It is recommended if there are not enough feature points to align chunks. The Camera based method uses estimated camera locations. It is advisable where known camera locations are highly accurate.

Fig. 17 Dense cloud reconstruction area

After the chunks are aligned, they must be merged. To do so, select the Merge Chunks command from the Workflow menu. In the window that opens, you need to select the chunks to be merged and mark the data types to be merged (usually, all data is merged to form an integral model). As a result, a new chunk with the merged data will appear in the resource tree.

1.7. Building dense point cloud When all photos are aligned, a dense point cloud can be built. In the course of the procedure, overlapping image pairs are compared pixel-by-pixel. Since the image orientation parameters are already known, for each point on an image its location on other images is known. If during the comparison that point is identified on both images, its three-dimensional coordinates are calculated (Fig. 17). Metashape automatically determines the reconstruction area of the dense cloud so that it includes all the objects displayed in the image. In the Model window, this area is marked with a grey parallelepiped (Fig. 18). 53

Fig. 18 Reconstruction area editing tools

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Since there are often more objects in the scene than necessary, it is advisable to reduce the reconstruction area to the boundaries of the desired area. This will allow the user to reduce the amount of redundant data and increase the reconstruction speed. You can edit the reconstruction area using the buttons on the toolbar. Using the Move Region, Resize Region, and Rotate Region tools, you can move, resize, and rotate the reconstruction area accordingly. After the reconstruction area is determined, you must execute the Build Dense Cloud command from the Workflow menu to build the dense point cloud. Set the reconstruction parameters in the pop-up window (Fig. 19).

Fig. 19 Dense point cloud parameter settings

Q uality determines the degree of downsizing images similarly to the Accuracy parameter that applies to photo alignment. High

Fig. 20 Dense point cloud visualisation

Surveying and modelling Jewish heritage sites. An ESJF guide

– original size images are used; Medium – images are downscaled by factor of 4, etc. The higher the reconstruction quality, the denser and more detailed the point cloud will be. D epth filtering. When comparing image pairs, the software calculates depth maps for each image. Due to some factors, like bad texture of certain objects, noisy or badly focused images, etc., the position of some points may be determined incorrectly, that is to say, there can be some outliers among the points. Such outliers are occasional and must be sorted out. Depth filtering has several modes, in particular Disabled, Mild, Moderate, and Aggressive. Aggressive filtration results in a smoother surface and is well-suited for reconstructing objects that do not have small details, for example, for aerial photogrammetric surveys of areas with smooth terrain. If the surfaces of objects are quite complex, it is advisable to use Mild filtration. Moderate filtration is used for intermediate objects. It is recommended to disable filtration only in the case of high-quality surveying with very good textures and no noise in the images. R euse depth maps – allows you to reduce the time to build a point cloud by reusing depth maps if they have already been built before. C alculate point colour – paints a point cloud in real colours from photos. C alculate point confidence – calculates how many depth maps have been used to calculate the coordinates of each point. It can be further used for point cloud filtering. After the operation is completed, a dense point cloud will appear in the resource tree. In order to display it in the Model window, you must select the appropriate button on the toolbar (Fig. 20).

Fig. 21 Point selection tools

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Fig. 22 Dense cloud point confidence visualisation

1.8. Point cloud filtering Often, after building a dense point cloud, it is necessary to filter out extra points, such as unwanted objects or just unreliable points that have appeared due to noise. This can be done in several ways: 1) Manual filtering. Use the selection tools on the toolbar (Fig. 21) to select extra points in the model space and delete them by pressing the Del button on the keyboard. 2) Filtering by colour. Using the menu Tools â€“ Dense Cloud â€“ Select Points by Colour, you can select all points of a certain colour (for example, noise points from the blue surface of a reservoir). Then delete them by pressing the Del button. 3) Filtering by confidence. If the confidence calculation was performed at the dense point cloud generation stage, points with 56

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low confidence can be removed from the model. To display the calculated point confidence, select the appropriate mode on the toolbar (Fig. 22). The point cloud will be coloured on a scale from red to blue depending on the number of depth maps, in accordance with the scale in the lower left. Using the menu Tools – Dense Cloud – Filter by Confidence, you can filter out points with low confidence.

1.9. Building polygonal model The next processing stage is building a polygonal model. This operation can be started by selecting the Build Mesh command from the Workflow menu and is controlled by the following basic parameters (Fig. 23):

Fig. 23 Basic polygonal model parameters

S ource data – data type used to create a polygonal model. Sparse cloud can be used for the rapid generation of a low-detailed model. In general, Dense cloud is used, which is generated and filtered at previous stages. Depth maps can be selected for arbitrary surfaces – this will allow all information from the photos to be used more efficiently but will not take dense point cloud filtering into account. S urface type – Arbitrary 3D can be used for reconstructing any objects. Height field 2.5D is optimised for modeling terrain and requires much less time and resources for the reconstruction. Q uality is only available for depth maps and specifies to what extent original images are downscaled, similar to the Quality parameter for building a dense cloud. F ace count specifies the maximum number of polygons in the model. For High, Medium, and Low options, Metashape automatically suggests the number of polygons depending on the point cloud density. The Custom option allows the user to enter a value manually (Fig. 24). 57

Fig. 24 Polygonal model generation results

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After the operation is complete, a polygonal model (3D Model) will appear in the resource tree. In order to display it in the Model window, you must select the appropriate button on the toolbar. Several visualisation types (shaded, solid, wireframe, etc.) are available for a model.

1.10. Model texturing A polygonal model obtained at the previous processing stage will be painted in real colours by painting the vertices of this model. This colourisation method provides good results where the model point density is very high. When the point density is low, the model will look blurry. The model visualisation quality can be improved by building a texture from photographs.

Fig. 25 Texture generation parameters

This operation can be started by selecting the Build Texture command from the Workflow menu and is controlled by the following basic parameters (Fig. 25): Usually, the following parameters must be set to build texture: T exture type – Diffuse map S ource data – Images M apping mode – Generic B lending mode – Mosaic T exture size/count – 4096х1 E nable hole filling – Enable E nable ghosting filter – Enable The Texture size/count parameter directly affects the level of texture detail – the larger the texture size, the higher the detail. A value of 4096 will generate a texture file with a size of 16 megapixels (4096x4096 pixels), which is enough for simple visualisation. If you need to build a more detailed texture, it is a good idea to increase the number of texture files, not the size of the texture itself, because creating a single high-resolution texture file requires a lot of memory and can produce an error. 58

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Fig. 26 Texture generation results

Other texturing parameters are necessary in special cases and are described in the Agisoft Metashape User Manual. As a result of the operation, the Textured model visualisation type will become available for the model (Fig. 26).

1.11. Building orthomosaic This operation can be started by selecting the Build Orthomosaic command from the Workflow menu and is controlled by the following basic parameters (Fig. 27): Usually, the following parameters must be set: P rojection â€“ in accordance with the coordinate system used in the project. S urface â€“ Mesh 59

Fig. 27 Orthomosaic parameter settings

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B lending mode – Mosaic E nable hole filling – Enable P ixel size – the smaller the pixel size, the more detailed the

Fig. 28 Orthomosaic generation results

orthophoto and the larger the file size. Metashape will automatically calculate the maximum possible resolution of the orthophoto depending on the detail of the photos. R egion – controls adjustment of the size of the area in which the orthomosaic is built. When the Setup boundaries option is turned off, an orthophoto will be created for the entire model. After the operation is complete, the orthomosaic will appear in the resource tree. Double-clicking on it in the resource tree will open the orthomosaic in the Ortho tab (Fig. 28). 60

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1.12. Building a digital elevation model Metashape allows users to quickly build a digital elevation model based on a dense point cloud for export to a GIS. Before performing the operation, you need to classify the ground on the point cloud using the

Fig. 29 Dense cloud with classified ground points

Tools - Dense Cloud - Classify Ground Points menu. The following parameters must be set in the pop-up window (suitable for most UAV survey scenarios): M ax angle – 15° (standard value for flat terrain). If the terrain is hilly or mountainous, the angle must be increased. M ax distance – 1 m. C ell size – 50 m. Upon classification, the dense point cloud can be coloured based on the assigned classes by selecting Dense Cloud Classes in the dense cloud display methods on the toolbar (Fig. 29). 61

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If certain areas of the cloud are incorrectly classified, the classification can be corrected manually by selecting the wrong area on the cloud using the Free-form Selection tool on the toolbar and assigning it the correct class in the Tools – Dense Cloud – Assign Class menu. To perform the DEM generation procedure, select the Build DEM command from the Workflow menu. The parameters are similar to those for building orthomosaics, except for the following:

Fig. 30 Digital elevation model generation results

S ource data – Dense cloud P oint Classes – Ground After the operation is complete, the DEM will appear in the resource tree. Double-clicking on it in the resource tree will open the DEM in the Ortho tab (Fig. 30). 62

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Fig. 31 Digital elevation model with corrected point classification

The resulting DEM will include Hillshade visualisation, which makes it easier to detect peaks and sharp terrain. If the peaks on the model do not match the actual terrain, it means that some points are incorrectly classified. In such cases, it is advisable to repeat the classification of dense cloud points with other parameters or manually correct the misclassified points and repeat the generation of the DEM (Fig. 31).

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1.13. Data export Metashape allows almost all generated results to be exported to external programs. This can be done via the File â€“ Export menu (Fig. 32) or by right-clicking on the corresponding object in the resource tree and selecting Export from the context menu. In the popup window, select the path to the folder in which the result and data format will be saved. Then in the next window select the export parameters (coordinate system, colours, texture, etc.). ! Particular attention should be paid to the coordinate system! Almost all GISs (ArcGIS, QGIS, MapInfo, etc.) can work with any coordinates. However, many 3D modeling programs cannot work with large coordinates, and therefore the results are mostly displayed incorrectly due to the loss of accuracy when using such programs. In this case, it is advisable to export the results in a local coordinate system with a certain offset along the X, Y, and Z axes. Fig. 32 Metashape export menu

2. Photogrammetric data processing in other software 2.1. Working with point clouds and 3D models in CloudCompare CloudCompare is an open source software for working with point clouds and 3D models. Initially, it was developed only for comparing point clouds, but later it significantly expanded its functionality and began to include many advanced algorithms (registration of point clouds, calculation of normals, statistical analysis, segmentation, etc.).

2.1.1. Working with large coordinates When objects with large coordinates (more than 105) are opened in the program, the program will warn the user and offer to move (or scale) the object so as to work in the local coordinate system with smaller coordinates (Fig. 33). 64

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Fig. 33 CloudCompare coordinate conversion window

This is because CloudCompare and the OpenGL specification work with 32-bit number values. This increases data processing speeds and reduces memory consumption by 50% compared to 64-bit values. However, the 32-bit representation has limited resolution (accuracy). It is therefore very important to move objects to local coordinates; otherwise, the accuracy of the original data will be lost. CloudCompare stores the move and scale values as meta data during processing. When the user saves the results, these values can be used to restore the original coordinate system (if the data format supports 64-bit values). If you need to remove large coordinates (for example, to work further in another software that does not support large coordinates), select the appropriate object and choose Edit Global Shift and Scale from the Edit menu. Change the GlobalShift value to zero 65

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in the pop-up window (Fig. 34). The Keep original position fixed option must be disabled.

2.1.2. Data import and export CloudCompare works with many point cloud data formats and 3D models. Therefore, it can be successfully used to convert three-dimensional data. However, when working in combination with other software, you should

Fig. 34 Removal of large coordinates in CloudCompare

pay attention to the following aspects: File formats B IN â€“ an internal binary data format used by CloudCompare. It stores the maximum amount of information and is able to store large coordinates, but it opens only through CloudCompare. 66

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E 57 – a popular format for storing point clouds. It can store large coordinates. D XF – an open file format for the exchange of graphic information between different CADs, which was created by Autodesk. It can store large coordinates. When imported into CloudCompare, only 2D data is displayed. P TS – a text format for storing point clouds. It can store large coordinates. P LY – a file format for storing point clouds and 3D models. It can be both binary and text. Binary files open and save much faster than text files. It can store large coordinates. F BX, OBJ, STL – popular formats for storing 3D models. They cannot store large coordinates. Features of interaction with other popular software A utodesk ReCap. The fastest way to transfer point cloud data from CloudCompare to Autodesk ReCap is to use the E57 format. A utodesk AutoCAD. CloudCompare does not process 3D DWG or DXF files. The easiest way to transfer a 3D model from AutoCAD to CloudCompare is to export it to STL or FBX format. A utodesk Meshmixer. It cannot work with large coordinates. Does not read PLY files created in CloudCompare. It is better to use STL and OBJ formats when importing data into Meshmixer.

2.1.3. Point cloud filtering CloudCompare has several tools to filter point clouds: I nteractive cloud segmentation S tatistical Outlier Removal 67

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Fig. 35 Interactive point cloud segmentation in CloudCompare

Interactive segmentation is used when it is necessary to separate extra objects from a point cloud. To start the segmentation procedure, select on the toolbar. Segment from the Edit menu or press the similar button An additional toolbar will appear in the model window in the upper right corner (Fig. 35). By default, the segmentation operation is started in polygonal selection mode. You need to draw a polygon in the model window: sequentially specify the polygon points with the left mouse button and finish the procedure with the right mouse button. If you then left-click in the model space, the polygon building procedure will start over. After building the polygon, select the segmentation mode on the toolbar: Segment in Segment out

â€“ leaves only those points that are inside the polygon; â€“ leaves only those points that are outside the polygon.

After the selection, the extra points will disappear and the segmentation will be suspended. It is also possible to change the camera posi68

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tion and estimate the segmentation quality in the model space. If the model still has extra points, you need to continue the segmentation: 1) Set the new camera position 2) Click the Pause Segmentation 3) Determine the new polygon.

button on the toolbar

When only the necessary points are displayed in the model space, you can complete the cloud segmentation in two ways: 1) The button confirms the segmentation and creates two clouds, one of which contains all the selected points and the other contains the rest of the points. 2) The button confirms the segmentation and creates only one cloud with the selected points, and the remaining points are deleted. Statistical Outlier Removal The Statistical Outlier Removal tool is used to delete occasional outliers. The operation is designed for calculating the distances from each point to its nearest neighboring points. If the distance significantly exceeds the average distance between the points, the corresponding point is deleted. The operation starts from the Tools – Clean – SOR Filter menu or by button on the toolbar and is controlled by two parameters: clicking the umber of neighbors. The more neighboring points are ana N lysed, the less likely it is that a point will be deleted because more distant points will be involved in calculating the distances. It is therefore advisable to set high parameter values on dense point clouds. Standard deviation multiplier threshold. If a point cloud has a uniform density, it is recommended to leave the coefficient value at 1. If the distribution of the points is not uniform, it is recommended to increase the coefficient. It is not recommended to set the value of the coefficient to less than 1 because, in this case, the “useful” points are likely to be filtered too. 69

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As a result of the operation, a new point cloud with the suffix “.clean” will appear in the resource tree.

2.1.4. Building elevation model CloudCompare allows users to easily model the ground surface with a regular grid (GRID). Before carrying out the operation, it is recommended to perform rough segmentation of the ground surface (see Sec. 2.1.3). Any noise points that are below the ground must be removed. To perform the DEM generation procedure, use the Tools – Projection – Rasterize menu or click the respective button on the toolbar. The purpose of the operation is to find the lowest point in each cell of the GRID – as a rule, this point corresponds to the ground surface. Set the following parameters in the pop-up window (Fig. 36):

Fig. 36 Rasterize tool settings window

tep – a grid step of the GRID model. It depends on the density S of points of the model and the shape of the terrain. The larger

Surveying and modelling Jewish heritage sites. An ESJF guide

Fig. 37 Rasterize operation results

the terrain variations, the smaller the grid step needs to be set for the DEM to be displayed correctly. It is not recommended to set a grid step that is less than the distance between the model points on the cloud. For most topographic purposes, a grid step of 0.5 m to 5 m is sufficient. Direction – Z Cell height – minimum Empty cells fill with – leave empty Then click Cloud on the Export tab. A new point cloud with the suffix “.raster” will appear in the resource tree and in the model window. In addition to ground points, it will contain other points (for example, points on the roofs of buildings, if there were no other points within the cell) (Fig. 37). Such extra points must be filtered manually or using a SOR filter (see Sec. 2.1.3). The result will be a model that contains only ground points (Fig. 38). 71

Fig. 38 Ground point cloud obtained after filtration

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Fig. 39 Polygonal elevation model generation results

To create the ground surface based on such a point cloud, you must execute the command Edit – Mesh – Delaunay 2.5 D (XY plane) with the value Max edge length = 0. As a result, an elevation model in the form of a polygonal surface will be built (Fig. 39).

2.1.5.Point cloud rendering CloudCompare can export point cloud visualisations as images or videos.

Fig. 40 Image rendering i n CloudCompare

To render a point cloud image or model, select the Render to file command from the Display menu. In the pop-up window (Fig. 40), set the path to the image file (File Name) to be generated and the zoom ratio (Zoom), if you want to generate an image with a resolution higher than that of the current image on the screen. To render a video, set the desired angle and select the Save viewport as object command from the Display menu or press Ctrl+V on the keyboard. The saved camera position will then appear in the resource 72

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tree (Fig. 41). Then set the next camera angle and save it again. Repeat the operation for all camera positions. To start video rendering, select all saved cameras in the resource tree and execute the Animation command from the Plugins menu. Set the animation parameters in the pop-up window (Fig. 42): A nimation – Total duration – total video duration in seconds. C urrent step – Duration – video duration for the selected camera position. L oop – return to the first angle after the last one (animation loop). I n the Video Output block you can set video file parameters, such as frame rate, bitrate, resolution, and file path. To render point clouds, it is recommended to set the bitrate to at least 25,000 kbps to avoid artifacts in the video. By clicking on the Preview button, you can see what the animation will look like in the model window. To create a video file, click the Render button. The operation may take some time, depending on video duration. The program will confirm the operation success with a pop-up window.

Fig. 41 Saved camera angles in CloudCompare

2.2. Autodesk ReCap Autodesk ReCap software is designed for working with point clouds. It allows users to import point clouds for viewing and editing as well as integrate them into other Autodesk products. The program converts point cloud source data from various formats into indexed clouds in RCS format, which are combined in a project file in RCP format. One RCP project file can contain many RCS point clouds. Thanks to the indexing mechanism, ReCap can quickly process massive clouds (billions of points).

Fig. 42 НVideo rendering settings in loudCompare

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2.2.1. Point cloud data import into Autodesk ReCap In order to import point cloud data, you must first create a new project. To do so, click on the New project button on the main screen of the program or press Ctrl+N on the keyboard. Select Import point cloud in the pop-up window (Fig. 43). In the next window, enter the project name and the path to the folder where the project files will be saved and click the Proceed button. Then select the point cloud files to import. Use the Select files for import and Select folder for import buttons to specify the path to a specific file or file folder, or you can transfer cloud files directly from File Explorer to the ReCap window. After you select the files, the import settings window will open with two tabs: F iltering – designed to configure the filtering of laser scanning point clouds. Advanced (Fig. 44) – additional import parameters. Decimation grid – decimation of a cloud to the specified

Fig. 43 Creation of a new project in ReCap

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Fig. 44 Point cloud import settings window in ReCap

distance between points. All points at a shorter distance will be deleted. The standard value is 5 mm (sufficient for architectural and planning projects). C oordinate system – conversion of the point cloud coordinate system (current – source coordinate system, target – target coordinate system). If the coordinate system does not need to be converted, the fields should be left blank. p axis – selection of the upward axis. By default, it is the U Z axis. After entering the import parameters, click the Import files button. The list of files to import will be displayed in the next window. If necessary, you can add more files to the project using the Select files for import and Select folder for import buttons on the left side of the window (Fig. 45). 75

Fig. 45 Data import window in ReCap

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When all files are selected, click Index files. As soon as the indexing is over, the project will be saved automatically. Click the Launch project button to view the project.

Fig. 46 Visualising point in ReCap

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2.2.2. Data visualisation parameter settings ReCap supports several methods of visualising point clouds, between which you can switch in the Display settings – Colour Mode menu (Fig. 46): R GB – real point colours obtained from photos. E levation – altitude map. Paints the cloud in the colours of the spectrum from red (lowest point) to pink (highest point). I ntensity – the intensity of the reflected laser beam. Displayed only for laser scanning point clouds. N ormal – normals to the surface. When importing point clouds, ReCap analyzes the curvature and orientation of surfaces on the cloud and paints differently oriented surfaces in different colours. In this mode, the edges of the faces of surfaces stand out very well. a) Scan location – paints each cloud in a different colour. The following additional parameters are also available: D isplay settings – Points – Point size allows resizing cloud points during visualisation. D isplay settings – Toggle UI Elements – Perspective allows switching between isometric and perspective projection.

2.2.3. Point cloud cleaning In ReCap, point clouds can be cleaned manually using the Selection tool located on the bottom toolbar (Fig. 47). Three point selection modes are available: W indow – selection by rectangle. You must specify two opposite corners of the rectangle. F ence – selection by a polygon. All points of the rectangle must be specified sequentially. To end the selection, press Enter or double-click the left mouse button. 77

Fig. 47 Selection tool toolbar

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P lane – selection of points that are on the same plane. This activates the Plane depth parameter, which determines the permissible deviation of points from the ideal plane. To determine a plane, you must specify at least 3 points on a cloud that belong to a plane and press Enter.

Fig. 48 Deleted points restoration menu

The selected points will be automatically highlighted on the cloud. When the operation is repeated, all previously selected points are deactivated. If you make a selection with the Shift key pressed, the new selection will be added to the existing one. If you press the Alt key, the points will be subtracted from the previous selection. Fig. 49 ReCap image export menu

When the points are selected, additional buttons will appear on the bottom toolbar: R egion – allows users to distribute points by regions so that only the points of a particular region can be hidden further. C lip – allows users to hide a portion of the points. The Clip outside option will leave only selected points active, while Clip inside will leave all points except for the selected ones. The tool does not delete points but only temporarily hides them. The Unclip button will return all the hidden points to the screen. D elete – deletes the selected points C lear – cancels the selection. It should be noted that when unnecessary points are deleted using the Delete tool, they still remain in the project file and can be restored via the Recover deleted points option in the main menu (Fig. 48).

Fig. 50 Image export parameters

After all point cloud modifications are completed, you need to save the project by selecting the Save button from the main menu or by pressing Ctrl+S on the keyboard. ReCap will ask whether it is necessary to optimise the project. If you select Optimise, all deleted points will be permanently lost. If the project is not optimised, the deleted points can be restored in the future. 78

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Fig. 51 Project navigation bar

2.2.4. Point cloud rendering ReCap can export point cloud visualisations as images or videos. To render the current point cloud visualisation as an image, select the Export Image command from the main menu (Fig. 49) or press Ctrl+J on the keyboard. In the pop-up window (Fig. 50), specify the image format, resolution, path to the image folder, and file name. A point cloud can be rendered as a video from ReCap using the following method: 1)

Open the project navigation bar by clicking the Project Navigator button in the lower right corner or by pressing the Q key on the keyboard (Fig. 51).

2) Set the desired camera angle and click the â€œ+â€? button under View States on the project navigation bar (Fig. 52). 79

Fig. 52 Saving camera angles

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Fig. 53 Exporting point cloud video from ReCap

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Repeat the operation for each desired camera position. Saved cameras will be displayed as a list under View States. By clicking the Play animation button under View States, you can view the camera’s trajectory generated from the saved angles. 3) Click the Export animation button under View States (Fig. 53). And set export parameters (Fig. 54). Video export is controlled by the following parameters: P ause time – camera delay time on each saved angle in seconds. T ransition time – time of transition between angles. L oop – return to the first angle after the last one. E asing – smoothing the camera trajectory. F ormat – output video file format R esolution – video file resolution. P ath – the path to the save folder F ilename – video file name. Click the Export button after setting the parameters. When the rendering process is completed, ReCap will prompt you to open the folder containing the file for viewing.

2.3. Autodesk AutoCAD Civil 3D Fig. 54 Video export parameter settings

AutoCAD Civil 3D software is designed for professionals in land management, surveying, spatial planning, and infrastructure. It is based on the use of information modeling technology, which allows users to significantly automate the design of objects.

2.3.1. Working with a point cloud in AutoCAD Civil 3D Working with a point cloud in Civil 3D is similar to other Autodesk software products. A point cloud is not imported directly into a Civil project, only the link to a ReCap project is saved. 80

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Fig. 55 Importing a point cloud into Civil 3D

Fig. 56 Point cloud visualisation in Civil 3D

To load the cloud, select Attach on the Insert tab in the Point Cloud block (Fig. 55) and then choose the path to the ReCap project with the cloud. In the next pop-up window, you need to set the parameters for inserting the cloud, namely the insertion point, scale, and rotation. If the point cloud is already in the required coordinates, the insertion point must have the coordinates X=0, Y=0, Z=0. The viewport automatically visualises the point cloud (Fig. 56). 81

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Fig. 57 Point cloud control pane in Civil

If you select the loaded point cloud in the viewport, the Point Cloud tab with the toolbox is automatically activated on the ribbon (Fig. 57). The main tools for working with the cloud are described in the table: Display block Point Size

The size of cloud points. Adjusted visually for a better perception of the point cloud.

Level of detail

The level of detail of the cloud. A default value of 10 means that all points are loaded. With large data sets, this can significantly slow down the work. When the level of detail is reduced, some points will be discarded, which will speed up the work.

Visualisation block Stylisation

A method of point cloud visualisation similar to ReCap (see Sec.Â 2.2.2)

Transparency

Adjusting the transparency of the point cloud

Cropping block Crop

Cropping the point cloud. Rectangle, polygon, or circle cropping is available. Used when you want to temporarily highlight a certain area on the cloud.

Uncrop all/last

Undo all/last cloud cropping.

Section block

Designed to crop a point cloud in a plane.

Surveying and modelling Jewish heritage sites. An ESJF guide

Extract block

Designed for semi-automatic recognition of edges, corners, and axial lines on a point cloud. Works only for structured laser scanning point clouds.

Options block Point Cloud Manager

The point cloud manager. Allows hiding specific regions or individual clouds if they are present in the ReCap project.

External Reference

The pane for working with external references. Allows various external files to be loaded or deleted from the project, including point clouds, PDF documents, images, and more.

2.3.2. Building digital elevation model A digital elevation model based on a point cloud can be built in different ways in Civil 3D. 1) Building DEM directly from point cloud. To perform the operation, select Surfaces â€“ Create Surface from Point Cloud on the Home tab in the Create Ground Data block (Fig. 58). Set the following parameters in the pop-up window: G eneral surface parameters: name, description, style, material, and layer. S election of the point cloud: setting the selection of the area from the cloud on which the surface will be built as well as the surface point density (Distance between points). M ethod of filtering non-ground points. 83

Fig. 58 Building DEM from point cloud

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Fig. 59 Surface parameter settings

P lanar average – discards all points above the middle plane. K riging interpolation – interpolates new points and builds a curved surface. Discards points above this surface. Runs slower than Planar average, but the filtering quality is much higher. N o filter – without filtering. Suitable where the point cloud has already been prefiltered. After setting the parameters, click the Create surface button (Fig. 59). The surface creation time depends on the size of the point cloud. After the surface is built, it is automatically visualised in the viewport (Fig. 60). Advantages of building a DEM directly from a point cloud: T here is no need to preprocess the point cloud. 84

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Fig. 60 Results of building surface from point cloud

Fig. 61 Fragment of digitised structural points of the terrain

Disadvantages of the method: M any polygons in the model. I naccuracy of the model in places where there are shadows or holes on the point cloud. 85

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2) Building structured elevation model To create a structured elevation model, you must first digitise the characteristic terrain lines (watersheds, thalwegs, ledges, and toes) in the form of 3D polylines, lines, and splines using standard AutoCAD tools. All these lines must be correctly positioned on the plan and in height (Fig. 61). It is also necessary to digitize the feature points of the terrain. To perform the operation, select Points – Create points-Miscellaneous – Manual on the Home tab in the Create Ground Data block.

Fig. 62 3D referencing settings

Next, specify a point on the terrain. For the point to be exactly in accordance with the cloud, enable 3D referencing. To do this, run the 3DOSNAP command on the command line. Activate 3D Object Snap On and Point Cloud – Node in the pop-up window (Fig. 62). ! When a point cloud has large coordinates, the cloud referencing may not work properly. In this case, it is recommended to move the point cloud to local coordinates (e.g. using CloudCompare, see Sec. 2.1.1) and insert it in Civil specifying the move parameters in the Insertion point field.

When creating a new Civil point, you must enter its short description (e.g. “ground”) and the elevation mark. If the cloud referencing worked correctly, the mark will be detected automatically and will already be entered in the field. The created points will be automatically displayed in the model space in the Top view (Fig. 63). After digitising the feature points, you need to group them. To do so, right-click on the Point Groups item on the Toolspace pane and select New... from the context menu (Fig. 64). Fig. 63 Digitised structural lines and terrain points

In the point group settings pop-up, specify the following options: On the Information tab, in the Name field, specify the name of the point group, such as Ground Points. 86

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O n the Include tab, in the With raw description matching field, enter the same short description of the points that was specified when they were created. Thus, all ground points created earlier will be automatically included in this group. If new ground points are digitised after the group is created, you must right-click on the group name in the resource tree on the Toolspace pane and select Update to update the list of the points. After the digitisation of the feature points and lines of the terrain, it is necessary to build the surface. To do this, perform the following sequence of actions: 1) On the Home tab under Create Ground Data, select Surfaces – Create Surface, or right-click on Surfaces on the Toolspace pane and select Create Surface. Select TIN Surface in the Type field in the pop-up window. The newly created surface will be displayed in the resource tree on the Toolspace pane. 2) Expand the surface structure in the resource tree and rightclick on the Definition – Breaklines group and select Add from the context menu (Fig. 65). 3) In the pop-up window for adding structural lines, specify the following parameters:

Fig. 64 Creation of a new point group

D escription – description of a group of structural lines (arbitrary). T ype – Standard. S upplementing factors control the further splitting of structural lines to build a more regular surface. They are set depending on the required level of detail and density of points. For large-scale surveying, 1 m is usually enough. D istance – controls the distance into which the rectilinear sections of structural lines are divided. M id-ordinate distance – controls the distance from the arc to the chord when simplifying the curvilinear elements. 87

Fig. 65 Adding structural lines to the surface

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Fig. 66 Structural elevation model generation results

4) Right-click on the Definition â€“ Point Groups group and select Add... from the context menu. 5) In the pop-up window, select the group of ground points you created earlier. 6) Right-click in the resource tree on the created surface and select Rebuild from the context menu. The newly created surface will be displayed in the model space (Fig. 66). The created model can be refined at any time by adding new structural lines or feature points to the surface. Advantages of the structured DEM: A small number of polygons High accuracy Disadvantages of the method: The need to digitise manually the structural lines of the terrain

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1. Creating new project To create a new project: S tart Pix4Dmapper. S elect Project> New Project ... on the menu bar (Fig. 1). N ew Project window opens (Fig. 2). E nter the project name in the Name field. ( optional) Click Browse... in front of Create in field. Browse to select a folder to save the project and results and click Select Folder in the Select Project Location pop-up. ( optional) To save all new projects in the selected folder, select Use As Default Project Location.

Fig. 3 90

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S ave New Project with default parameters in Project Type. C lick Next.

Importing images In Select Images window: T o add an image click Add Images... (Fig. 3). S elect the folder in which the images are stored in the Select Images pop-up, select images for import (multiple images may be selected) and click Open. C lick Next. Vertical coordinate system should be changed from default geoid height to ellipsoidal height (EGM96) in the Image Properties Editor. To do this: O pen the Select Image Coordinate System editor.

Fig. 1

C lick on Advanced Coordinate Options. S elect Geoid Height Above WGS 84 Ellipsoid (0m). C lick ОK.

Fig. 2 91

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Selecting Output/Ground Control Points (GCP) Coordinate System In the Select Output Coordinate System window (Fig. 4): (optional) Change Output/GCP Coordinate System. ! Note:

By default, the Output Coordinate System is the same as the Ground Control Points (GCP) Coordinate System. Therefore, the results may be displayed in the Ground Control Points (GCP) Coordinate System.

By default, the Unit is m (metres). If the images have geolocation, Auto detected is selected by default, displaying the corresponding UTM or NAD83 zone of the images.

If the images do not have geolocation, by default, Arbitrary Coordinate System is selected.

Click Next (Fig. 5).

Fig. 4

Surveying and modelling Jewish heritage sites. An ESJF guide

Selecting processing options template In the Processing Options Template window: Select the necessary template based on the desired application and results described in the Processing options template section. ( optional) Select Start Processing Now to start processing automatically (Fig. 6). Click Finish to close the wizard and start the project.3.2

Fig. 5

Fig. 6

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II. Processing (Step 1)

Fig. 7

Processing parameters Enter project settings (Fig. 7). For the first step of processing, select the following parameters: Calibration method: Standard. Initial parameters optimization: All (Fig. 8). Tick 1. Initial processing and select Start (Fig. 9). Fig. 8

After processing, a point cloud will appear in the RayCloud window (Fig. 10).

Surveying and modelling Jewish heritage sites. An ESJF guide

Fig. 9

Fig. 10

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Adding ground control/checkpoints

Fig. 11

To import ground control points, open GCP Manager (Fig. 11).

Fig. 12

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Fig. 13

Set GCP Coordinate System (Fig. 12). Select GCP file (*.txt,*.csv) (Fig. 13).

Fig. 14

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Fig. 15

To use the imported points as check points, select Check Point in the Type field. Check points do not affect the accuracy of project processing and are used only for accuracy estimations (Fig. 14).

Fig. 16

Surveying and modelling Jewish heritage sites. An ESJF guide

To use the imported points as ground control points select 3D GCP. In this case they will affect the accuracy of the project processing.

Fig. 17

After the ground control points have been added, they must be marked in the project. To do so, enter RayCloud Editor (Fig. 15). Mark the imported points in the project. To do so, select the necessary point on the layers panel or select the point on the project interface (Fig. 16). Mark the point in the Images window in at least four images. utomatic Marking allows a point to be automatically marked in all A the available images. S02 [pixel] is a parameter, which defines pointing accuracy in pixels. Select Apply to save changes (Fig. 17). Repeat for all the available points. 99

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Fig. 18

Project optimisation To accept the changes described in the previous step, carry out project optimization (Fig. 19).

Fig. 19

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Fig. 20

After processing the project, create a Quality report (Fig. 20). If the report meets the necessary accuracy criteria, proceed with further processing steps.

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III. Processing (Step 2) Click Processing Options and select the necessary parameters for point cloud processing (Fig. 21). To start processing, untick initial processing and tick 2. Point Cloud and Mesh (Fig. 22). ! Note: If a completed step is not unticked, it will be reprocessed, and all the saved results will be overwritten with the new information.

Fig. 21

Fig. 22

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Working with point cloud

Fig. 23

To visualise a point cloud, select Point Clouds in the Ray Cloud window after processing. To visualise a 3D surface, activate Triangle Meshes (Fig. 23).

Fig. 24

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Fig. 25

Fig. 26

Editing a point cloud is necessary for building a Digital Terrain Model and enhancing the quality of a Digital Surface Model or orthomosaic (Fig. 24). 104

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Fig. 27

Select the editing tool on the top toolbar. Select the necessary point group and a layer to transfer it to (Fig. 25). For convenience, use Clipping box. This tool does not affect project data and is used only for visualization and simplification of data processing (Fig. 26, 27).

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IV. Processing (Step 3) After editing the point cloud, it is necessary to carry out the third processing step, on which the orthomosaic, DTM, DSM, and other data are based.

Fig. 28

Fig. 29

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Fig. 30

Processing parameters are set in the Processing Options window (Fig. 28, 29). After processing, the Mosaic Editor and Volumes panes become active (Fig. 30).

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UAV data processing in Pix4D software in Phantom 4 RTK drone records

Chapter 3

Processing results The results of all the processing steps are saved in the directory set when creating the project. To browse the directory, click on the corresponding pictogram (Fig. 31). Saved results may be used in other software products for further processing.

Fig. 31

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Authors

Tetyana Kondratenko is the head of the geodetic department of DroneUA. From the very beginning of the development of drone technology for industrial use, DroneUA has successfully integrated UAV technology into a variety of specialised areas, such as architecture, geodesy, industry, and more. Tetyana is a certified product specialist with Pix4D software.

kondratenko@drone.ua

Denis Gorkovchuk is an Associate Professor of Geoinformatics and Photogrammetry at Kyiv National University of Construction and Architecture, where he is a PhD candidate in Technical Sciences. His research focuses on the data integration of laser scanning and photogrammetry, virtual and augmented reality, and the automation of three-dimensional modelling. He is a co-founder of the international company SPM3D LLC, which specialises in processing data obtained from laser scanning and photogrammetry. He can be reached at.

d.gorkovchuk@gmail.com

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Геодезичні дослідження та моделювання об’єктів єврейської спадщини

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