Object Detection Techniques Applied to UAV Photogrammetric Survey

This project proposes an automated approach to the census of technological and architectural elements from massive photography datasets; it is built on photogrammetric close-range acquisitions performed via UAV over the roofs of the centre of Bethlehem and aims at mapping the water tanks for civilian use that create loads on historical buildings in a seismic area. The urban census was conducted within “3D Bethlehem. Management and control of urban growth for the development of Heritage and Improvement of life in the city of Bethlehem”, a project promoted by AICS. Deep Learning models were built on a Cloud Infrastructure handling model lifecycle from training to deployment. Tests were conducted on historical buildings that show multiple spurious elements such as debris and junk creating occlusion on objects of interest. Such density creates complex scenarios for models targeted at assisting large scale monitoring and management of the areas for different teams and municipalities.