Topology-Enhanced Urban Road Extraction via a Geographic Feature-Enhanced Network

Urban road extraction has wide applications in public transportation systems and unmanned vehicle navigation. The high-resolution remote sensing images contain background clutter and the roads have large appearance differences and complex connectivities, which makes it a very challenging task for road extraction. In this article, we propose a novel end-to-end deep learning model for road area extraction from remote sensing images. Road features are learned from three levels, which can remove the distraction of the background and enhance feature representation. A direction-aware attention block is introduced to the deep learning model for keeping road topologies. We compare our method on public remote sensing data sets with other related methods. The experimental results show the superiority of our method in terms of road extraction and connectivity preservation.