Object Detection Techniques Applied to UAV Photogrammetric Survey

This project proposes an automated approach to the census of technological and architectural el-ements from massive photography datasets. This use case is built on photogrammetric close-range acquisitions performed via UAV over the roofs of the centre of Bethlehem, in order to map 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. The pre-sented work leverages the project dataset to train Deep Learning models on a Cloud Infrastructure handling model lifecycle from training to deployment. Tests were conducted on historical buildings that show, among objects of interest, multiple spurious elements such as debris and junk. Such density creates complex scenarios for models that are trained to automate recurrent operations to assist large scale monitoring and management of the areas for different teams and municipalities.