Bi-HRNet: A Road Extraction Framework from Satellite Imagery Based on Node Heatmap and Bidirectional Connectivity

Abstract

Today, with the rapid development of the geographic information industry, automatic road extraction from satellite imagery is a basic requirement. Most existing methods have been designed based on binary segmentation. However, these methods do not consider the topological features of road networks, which include point, edge, and direction. In this study, a topology-based multi-task convolution network is designed, namely Bi-HRNet, which can effectively learn the key features of nodes and their directions. First, the proposed network learns the node heatmap of roads, and then the pixel coordinates are extracted from the node heatmap via non-maximum suppression (NMS). At the same time, the connectivity between nodes is predicted. To improve the integrity and accuracy of connectivity, we propose a bidirectional connectivity prediction strategy, which can learn the bidirectional categories instead of direction angles. The bidirectional categories are designed based on “top-to-bottom” and “bottom-to-top” strategies, which can improve the accuracy of the connectivity between nodes. To illustrate the effectiveness of the proposed Bi-HRNet, we compare our method with several methods on different datasets. The experiments show that our method achieves a state-of-the-art performance and significantly outperforms various previous methods.

Keywords

• road extraction
• multi-task learning
• node heatmap
• bidirectional connectivity