Pushing the Limits of Sentinel-2 for Building Footprint Extraction

Building footprint maps are of high importance nowadays since a wide range of services relies on them to work. However, activities to keep these maps up-to-date are costly and time-consuming due to the great deal of human intervention required. Several automation attempts have been carried out in the last decade aiming at fully automatizing them. However, taking into account the complexity of the task and the current limitations of semantic segmentation deep learning models, the vast majority of approaches rely on aerial imagery (<1 m). As a result, prohibitive costs and high revisit times prevent the remote sensing community from maintaining up-to-date building maps. This work proposes a novel deep learning architecture to accurately extract building footprints from high resolution satellite imagery (10 m). Accordingly, super-resolution and semantic segmentation techniques have been fused to make it possible not only to improve the building's boundary definition but also to detect buildings with sub-pixel width. As a result, fine-grained building maps at 2.5 m are generated using Sentinel-2 imagery, closing the gap between satellite and aerial semantic segmentation.