BMDCNet

Multi-level Road Feature extraction from remote sensing image plays an important role in numerous applications such as autonomous driving and urban planning. However, interference from background, occlusions and road-like information making it difficult to distinguish different levels of roads. To address these issues, this study proposes a deep network named BMDCNet, which adopts LinkNet as the baseline model. The Bidirectional Multi-level Road Feature Dynamic Fusion (BMDF) module is designed to replace the simple skip connections, which greatly reduce the semantic gap between the encoder and decoder. Furthermore, the Dual Context Dynamic Extraction (DCDE) module is designed to dynamically extract and integrate global and local multi-scale context information. Finally, experiments are conducted on the DeepGlobe Road Extraction Dataset and the Massachusetts Roads Dataset. The results demonstrate that compared with LinkNet, the F1 and IoU of BMDCNet increased by 1.79% and 2.68% on DeepGlobe, and by 0.35% and 0.47% on Massachusetts, respectively.