Change detection is the process of identifying differences in the state of a region by observing it at different times. Multitemporal Remote Sensing (RS) data, such as satellite and aerial imagery provide abundant information to identify structural differences in a specific area across a window of time. However, manual inspection of the high volume of data produced by RS sources is slow and error-prone, and existing solutions for automatic change detection struggles to capture small changes in buildings and terrain.The main goal of this R&D effort is to design a UAS change detection tool with advanced AI and state estimation capabilities allowing for the georeferenced localization and classification of changes in structure and terrain by comparing FMV data with a reference 3D map
