Road Graph Extraction via Transformer and Topological Representation

Road graph extraction from remote sensing images aims at extracting topological maps composed of road vertices and edges, which has broad prospects in urban planning, traffic management, and other applications. However, existing methods are easily affected by complex remote sensing scenes, and also have shortcomings such as poor continuity and slow processing speed. In this letter, we propose a novel end-to-end road extraction method named 'Road2Graph', which encodes road graphs into topological representations for prediction. We proposed a transformer-based model to encode the deep convolutional features, and then fuse them with the output of the feature extractor to make the network pay more attention to the global multiscale road topology context. We also design an efficient topological representation that encodes attributes such as road segmentation, midpoint map, vertex map, and connection relationships with few parameters and low redundancy. The obtained topological representation can be decoded to obtain the road extraction result in graph format. We conduct experiments on two public datasets - CityScale dataset and SpaceNet dataset. The results show that our method achieves the state-of-art and improves both accuracy (TOPO-F1 +1.55% on CityScale dataset and +2.23% on SpaceNet dataset) and continuity (APLS +7.03% on CityScale dataset and +3.05% on SpaceNet dataset) compared to the other methods.