Large-Scale Urban Road Vectorization Mapping Via A Road Node Proposal Network for High-Resolution Remote Sensing Imagery

Urban road vectorization mapping can reflect the urban development of cities, which consists of two separate tasks: road extraction and road vectorization. Most of the current road vectorization mapping methods focus on road extraction yet ignoring the importance of road vectorization, facing the problem of road connectivity. In this work, to implement urban road vectorization mapping in a unified way, a novel road vectorization mapping framework is proposed. The proposed framework consists of a node proposal network (NPN) module and a node connectivity based road refinement module. In the NPN module, a node proposal head is adopted, which improves the connectivity of the road mask by providing supervision of the road nodes, which are actually part of the road mask. The road mask is then converted into a road vector map by vectorization. In the node connectivity based road refinement module, road nodes are inserted into the road vector map to improve the connectivity. The experimental results of two public datasets (SpaceNet 3 and DeepGlobe) confirm the advantages of the proposed framework. The experiments for two areas in Shanghai and Wuhan demonstrate the generalization ability of the proposed framework.