3D Face Reconstruction by Close Range Photogrammetry

~ By Rishi, 2nd Year, B.E. Geoinformatics

In present scenario, crimes are a common visual So, it is important to identify the face of the victim to proceed the investigation to regulate the crime rates. Photogrammetry helps the forensic domain in this problem. The science of photogrammetry is defined by collecting reliable information about the object by recording and interpreting the photogrammetric images. For this process, we require three components - photographs of the skull, Facial Soft Tissue Thickness (FSTT) data table and a software to deploy the data. Photographs are obtained by the method of Close-Range Photogrammetry (CRP) owing to its accuracy Using a high-quality digital camera, multiple photographs of the skull are recorded covering all the angles, sides and surfaces with a diffused light to avoid shadowing Black background is preferred usually Many software are available for this purpose, the most efficient and used being the AgiSoft Photoscan This software makes it easy to build a 3D model based on the input photographs and export in any desirable file format Blender software is used hand-in-hand with the Photoscan software to classify various structures and landmarks covering the face and the cranium (craniofacial) The data so generated is analysed with the FSTT dataset and the average is used to create the facial reconstructions. In this way, photogrammetric tools and techniques assist in a vital way for forensic applications involving face reconstruction and recognition.

Accounting BC ecosystems: Remote Sensing approach

Blue carbon - for carbon captured by the world's ocean and coastal ecosystems. Sea grasses, mangroves, and salt marshes along our coast capture and hold carbon, acting as something called a carbon sink. These ecosystems take up the carbon in the atmosphere and store them beneath the ground where it is not visible to the naked eye, but does exist. Healthy blue carbon ecosystems also provide habitat for marine species, support fish stocks and food security and sustain coastal communities When these systems are damaged, an enormous amount of carbon is emitted back into the atmosphere, where it can then contribute to climate change So, protecting and restoring coastal habitats is a good way to reduce climate change and conserve the coastal vegetation.

Remote sensing-based approaches have been proven effective for mapping and monitoring mangrove, seagrass, and salt marsh ecosystems by many studies. In addition to the satellite data, aerial photography has also been used for coastal monitoring Landsat data is by far the most widely used dataset to map and monitor tidal wetlands, operating from the visible to the near infrared portion of electromagnetic spectrum. Recent developments in drone technology, or UAVs (unmanned aerial vehicles) have opened up new possibilities in the field of remote sensing. Remote sensing technology, hence, provides a powerful tool for monitoring and accounting for carbon stocks in BC ecosystems By enabling efficient, accurate and cost-effective monitoring of these ecosystems, remote sensing can support global climate change mitigation. However, to use remote sensing for blue carbon accounting, careful data acquisition, processing and validation are required, and regular improvisations are required as and when new data is acquired

Bhuvan is marketed as an Indian Geo-Platform by ISRO (Indian Space Research Organization), initially released in the year 2012 It features services in three major domains, namely: Satellite Data, Thematic Services and Mobile Applications.

Bhuvan’s data catalogue comprises largely of Earth Observational (EO) satellite imagery, acquired by Indian satellites at a relatively high frequency, implying that the data has both spatial and temporal frames of reference. The data is made analysis ready through conformance to a set of native standards, centred around standardizing parameters such as spatial resolution, radiometry, ortho -rectification, spatial reference and image display among others That enables interoperability and analysis of the datasets within the platform The platform also seeks to implement crowd-sourcing of spatial data through its Mobile Application based services, which enables a stream of non-EO spatial data of increased granularity.

The platform features explicit metadata generation for the stored datasets, once again based on native standards for metadata. Targeted retrieval of datasets is enabled through means of ‘Bhuvan Data Discovery’ function on the platform. The thematic products on the platform, offer a wide range of products catering to terrestrial, atmospheric and ocean sciences This reflects capacity to perform ‘Exploratory Analytics’, with limited user involvement User-based analytics is limited to basic functionalities such as shapefile creation, colour composite generation or choropleth map creation The analytics infrastructure of the platform continues to be localized, with data being moved to the source of analytics

The platform supports 2D and 3D visualization, apart from time-series visualization of select historic datasets. The visualization platform is powered by a Cesium API.

In terms of ‘Intelligence Generation’, the platform hosts a good array of functionalities required for a minimum level of inference generation, although higher levels of ‘Intelligence Generation’ in the form of incorporating machine intelligence are yet to be reflected

Source: https://bhuvan nrsc gov in/home

Taking a life decision that is unconventional can be intimidating, and that's how I felt about choosing B.E. Geoinformatics. Although I didn't have a specific interest in this field, I recognized that the university was reputable and had its strengths in each department While exploring different courses during the admission process, I came across this program A friend of my father who holds a PhD in geoinformatics shared valuable insights with me about the course and the various job opportunities that would be available to me I also reviewed the course curriculum and found it fascinating and distinctive. My preference is to enjoy what I do rather than doin g what I like. Therefore, I am eagerly looking forward to learning new things and gaining exposure during my four years at IRS.

Environmental studies, geography, and maps have always been areas of interest to me My sister, who is an environmental engineer, recommended geoinformatics to me, even though it is not directly related to environmental studies I wanted to pursue something different from the standard core courses like CSE, IT, and ECE, which led me to explore the syllabus of GI I found it to be captivating and engaging Now, having completed almost two semesters, I can confidently say that I am delighted with my decision to pursue this course During my time at college, I have observed many seniors working with surveying instruments on the college ground. Learning beyond the confines of the classroom is enjoyable, and we are fortunate to be able to engage in practical fieldwork to gain hands-on experience I hope to have a productive and enlightening experience during my four years of study

There were two reasons why I chose the Geo Informatics course at CEG Firstly, I have always been fascinated by nature The curriculum of this course got me inclined towards it Secondly, it is believed thatthe most popular and preferred streams include IT, CSE and streams like Mechanical Engineering are believed to provide greater career opportunities. However, I wanted to challenge this notion by choosing a peculiar and lesser-known stream, and then excel in it

This course combines both theoretical sciences and technology, making it an appealing option for me Pursuing this B E degree would enable me to comprehend both engineering technology and geographic components. Growing up in a family of geologists, particularly in the mining sector, I gravitated towards studying earth sciences Initially, my primary expectation from this course was to secure a good job However, after attending the CELESTIA symposium, I gained a deeper understanding of what this field has to offer beyond just career opportunities. I aspire to learn as much as possible from this course and apply my skills to contribute to society

Role of Generative Adversarial Networks in Classification Algorithms

~ Chameli R., 3rd year, B.E. Geoinformatics

Remote sensing image classification involves understanding satellite or aerial images and identifying the land cover types present in the images. This is a challenging task due to the complexity involved, including variations in terrain, vegetation, and atmospheric conditions. Deep learning models have been successful in achieving high accuracy in remote sensing image classification tasks. These models are trained on large datasets of labelled images and use techniques such as data augmentation and transfer learning to improve their performance.

The accurate classification of remote sensing data using machine learning models largely depends on the accuracy of the training data and validation sets. Traditionally, such validation has been possible by ground-truth data. However, there is a lack of diverse/representative ground-truth data because collecting ground-truth data is an expensive, cumbersome task

To battle this, the remote sensing community finds itself a new saviour, Generative Adversarial Networks (GANs) GANs are a class of deep learning models capable of generating synthetic data They are trained with some real data which is used to learn important features that can then help synthesize data that looks like real data. This is important as GANs have shown to be powerful models in different computer vision tasks which are also common in RS.

A common example of a data deficient scenario is that of wetland mapping. With limited ground data, GANs can artificially generate the training data for classes of wetlands. The data generated from GANs and the real time observation is then used for testing the classification model This reduces the cost and the time spent in acquiring a large quantity of data and increases the accuracy of the model as an even number of data samples can be considered for different classes.

Besides wetland mapping, GANs can be used to assist a wide range of feature classification such as building extraction, roads extraction, object detection and scene classification. It is also widely used to reconstruct and restore images and finds applications in digital image processing and analysis. As drone images and aerial images become more common, it is imperative that we understand a way to process this high-resolution imagery as accurately as possible using an integrated approach of remote sensing, machine learning and deep learning techniques.