To address the challenges of weak connectivity， subtle branch omission， and topological inconsistency between predicted and real road networks in optical remote sensing image road extraction， this paper proposed a road extraction network with directional guidance and topological awareness. First， the multi-path directional guidance module was designed to model multi-directional connectivity relationships. By decoupling and independently learning connectivity features across distinct directions， this module enhanced inter-branch linkages and improved segmentation continuity. Second， the full granular complementary feature guidance module integrated fine-grained and coarse-grained features， reinforcing both road details and semantic representations to strengthen the network’s capability in capturing subtle branches. Finally， a topological awareness function was introduced to quantify geometric structural discrepancies from a topological perspective， thereby constraining the topological consistency between predicted and real road networks.The proposed model achieves F1 scores of 81.95% and 79.98% on the DeepGlobe and Massachusetts datasets， outperforming the state-of-the-art methods by 0.73% and 1.5%， respectively. The IoU metrics reach 69.35% and 66.38%， with improvements of 0.98% and 0.66% over existing benchmarks. Experimental results demonstrate that RDTA-Net significantly surpasses mainstream methods in both road extraction accuracy and completeness. Furthermore， it exhibits robust performance in complex scenarios involving occlusions， noise， and illumination variations.