Casdenet: Cascade Automatic Road Detection Network Based on Dynamic Snake Convolution and Edge Branch

Automated road detection from the high-resolution remote sensing images (RSI) is always a hot topic. Particularly, the accurate and continuous road detection in RSI is challenging due to the tree and shadow shading and unsmooth road edges further hindering the accuracy of the road extraction. Considering that the dynamic snake convolution (DSConv) is able to capture the distinctive characteristics of tubular objects such as roads, and edge information extracted from road edge branch can enhance the smoothness of road edges, we propose a cascade automatic road detection method based on DSConv and edge branch named CasDeNet. Specifically, the DSConv is introduced as the foundational module for low-level feature extraction of CasDeNet, aiming to capture the intricate shapes of roads. Road edge information from the edge branch is incorporated to ensure the smoothness of the road edges. Experiments are conducted on the Ottawa dataset. The results show that the proposed CasDeNet can extract more coherent and accurate roads compared to other state-of-the-art (SOTA) methods and achieve the best results.