Topology-Guided Road Graph Extraction From Remote Sensing Images

Road maps are widely used in traffic management, vehicle navigation, urban planning, and other fields. However, automatically extracting road graphs from remote sensing images is very challenging due to the interference of vegetation and buildings and the problem of data imbalance. In this article, we propose a novel end-to-end road graph extraction method for remote sensing images named “TopoRoad,” which learns vectorized representations of road maps guided by topological graphs of the roads. Our method decouples road graph extraction into the predictions of a vertex/degree map (DM), an orientation map, and a segmentation map, which are then jointly decoded to obtain the vertices and edges of the final road graphs. In the vertex branch (VB), we predict the probability map of the vertices for the subsequent vertex extraction. At the same time, to learn local topological connections, the number of connections for each vertex is predicted. For the orientation branch (OB), the connectivity between vertices is obtained by learning to predict the local extension direction of all road pixels. We conduct experiments on two public road extraction datasets—the CityScale dataset and the SpaceNet dataset. The result suggests that our method can produce accurate and continuous road graph extraction results with vectorized representations. Our method achieves state-of-the-art (SOTA) results compared to the other methods in terms of both local and global topological accuracy.