Road extraction from remote sensing imagery has been widely applied in fields such as navigation systems, disaster response, and autonomous driving. However, existing deep learning-based models suffer from insufficient capability to extract curved and slender roads, as well as challenges in balancing model complexity and performance. This paper proposes a novel road extraction method based on 32-channels U-Net model, named branch-enhanced directional sparse attention network (BDSANet), which sol