MCTN-Net: A Multi-Class Transportation Network Extraction Method Combining Orientation and Semantic Features

Transportation network extraction based on deep learning has become a hotspot. However, the existing models all aim to distinguish between background and transportation network, while ignoring the class attributes within the transportation networks. In this letter, we propose a multi-class transportation network extraction network (MCTN-Net) to simultaneously extract railways, roadways, trails and bridges. Inspired by multi-task learning, the network first extracts the semantic and information together by the use of a dense feature shared encoder (DFSE). The orientation and semantic features are then fused in the orientation-guided stacking module (OGSM) to enhance the connection between transportation network pixels. Furthermore, a semantic refinement branch (SRB) is designed to improve the ability of classifying different transportation network types through deep supervised fusion and class attention. A multi-class transportation network dataset was constructed and used in the experiments. The experiential results indicate that the proposed method achieves an MIoU of 64.29% and an FWIoU of 71.20% without the background, which is significantly better than the other road extraction models and semantic segmentation methods. The code and dataset are available at https://github.com/fzzfRS/MCTN-Net.