Graph Encoding based Hybrid Vision Transformer for Automatic Road Network Extraction

This paper introduces a graph encoding-based hybrid vision transformer for automatic road network extraction via high-resolution remote sensing imagery. Given that high-resolution remote sensing images covered large urban areas, traditional segmentation-based road extraction methods usually can generate good binary classification maps in simple structured road surfaces but fail in complex highway and bridge-covered areas. We introduce a graph encoding-based mechanism to address the above issues, enabling the road extraction framework extracts the road segmentation feature and build the graph structure map jointly. Compared to only segmentation-based methods, our approach learns prior geometrical structure information from the extracted ViT feature maps and has a non-local awareness of the whole road network structure. Eexperimental results demonstrated that the proposed approach outperforms the traditional segmentation-based methods.