The breakthroughs in Computer Vision applications have made it possible to automate this process, which can turn out to be extremely useful during situations of natural disasters and conflicts apart from issues related to urban planning and policy making.
Computer Vision has proven to be an excellent tool to analyze satellite images of any resolution, detect and classify objects and judge topographical features to provide an all encompassing and detailed feed to the end consumer. Aerial imagery of buildings is quite important for building up information about population distribution and city infrastructure planning including water and electricity lines, public transport, postal services etc. Real time and accurate satellite imagery of buildings and other topographical features gets most useful during risk assessment and relief distribution in the times of natural disasters. The contours of objects such as buildings, trees, water bodies and roads are assessed and labelled.
The network is trained through complex algorithms and a test model is prepared to accurately identify aerial images. Convolutional neural networks (separate for every class of object) are trained and outputs averaged with hyper parameters. Utilizing the expertise of our in-house senior data scientists and architects, we tested our model several times before furnishing the final product.