Rooftops or Footprints? Reliable Building Footprint Extraction From High-Resolution Satellite Images

Automatic footprint extraction from remote sensing images remains a popular yet challenging research topic, driven by numerous applications such as telecommunications and urban management. In this paper, we propose an automatic pipeline for reliable building footprint extraction from high-resolution satellite images. The proposed method takes as the input two images with the corresponding rational polynomial coefficient models, applies deep learning to each image for building segmentation and estimation of contour orientations, then matches extracted building footprints from two images for estimating reliability of each footprint. The pipeline has proven to be efficient for reconstructing accurate building footprints, and estimated reliability scores help to efficiently validate and correct predicted maps.