Residual Channel Attention Fusion Network for Road Extraction Based on Remote Sensing Images and GPS Trajectories

Digital roads are a crucial component of smart city development and sustainable urban development, being one of the fundamental geographic elements. Remote sensing images and GPS trajectories are two important data sources for obtaining road information, which can describe roads from complementary perspectives. Either of these data sources has its limitations in road extraction tasks but their fusion together can enhance road detection performance. However, previous work on the fusion between remote sensing images and GPS trajectories does not fully utilize the information of these two modalities, and both suffer from the problem of information loss. Therefore, we propose a deep convolutional neural network called residual channel attention fusion net (RCAF-Net) that fuses remote sensing images and GPS trajectories to extract road information more efficiently. We designed a residual attention fusion module to fuse the road features of both modalities from multiple layers and scales. In addition, we consider other attribute information, such as the speed and direction of the GPS trajectories, to provide richer semantic information for the road extraction task. The comprehensive experiments on two datasets show that RCAF-Net achieves about a 3% improvement in IoU and
F
1-score over the earlier data fusion. Our method fuses remote sensing images and GPS trajectories more efficiently than the existing methods and can extract more fine-grained and complete road information.