Deep Learning-Based Building Footprint Mapping Using High-Resolution SAR Data

Michael Recla, Michael Schmitt

Abstract: Investigating the synergy of deep learning and high-resolution Synthetic Aperture Radar (SAR) data, this paper focuses on building footprint extraction – a domain traditionally dominated by optical imagery. The proposed method involves projecting SAR data onto a digital terrain model and utilizing a modified U-Net for segmenting the building outlines in a common projected map system. An extensive data set consisting of TerraSAR-X images and OpenStreetMap building footprints was created to train the model. With the very promising results, the study positions SAR as a reliable alternative for accurate building footprint mapping, with implications for time-critical disaster management and urban monitoring.