Incorporating Building Information into Road Extraction from Remote Sensing Images

Road extraction is a critical task in the field of remote sensing, and is widely applied in various tasks. However, significant challenges arise in road extraction due to occlusions from surrounding objects and chaotic backgrounds. Signed Distance Map (SDM) of buildings contains distance information from pixels to building edges, which can serve as auxiliary information for road extraction tasks. To address these challenges, a Road-SDM Fusion Network (RSDMNet) is proposed to explore the latent relationship between roads and buildings to enhance contextual reasoning capabilities. By incorporating the distribution information of buildings, the proposed method provides additional prior features and more global information for road extraction. Experimental results demonstrate that the proposed method achieves a higher IoU and outperforms state-of-the-art approaches.