TERNformer: Topology-Enhanced Road Network Extraction by Exploring Local Connectivity
Bin Wang, Qingjie Liu, Zhenghui Hu, Wei Wang, Yunhong Wang
Abstract
Remote-sensing images provide us with rich information for extracting road networks. However, there are still great challenges ahead, such as occlusions caused by trees and shadows, and complex topology. In this work, we focus on the topology of road networks. Inspired by the observation that road networks are composed of road fragments in a bottom-up way and the breaks between fragments tend to be connected within a local area, we propose a Topology-Enhanced Road Network extraction (termed TERNformer) method by exploring local connectivity. First, a transformer-based network is built for road feature extraction to capture long-range context. Furthermore, we propose parallel depth-wise separable dilated convolution blocks (DSDB) to extract local information within different ranges. Thereafter, a minimum spanning tree-based local structure exploring block (LSEB) is built to enhance the topology of the road network. Finally, a simple but effective shortest-path-based method is used to refine the road network connectivity within a local threshold. Experiments conducted on two datasets demonstrate the superiority of TERNformer. TERNformer outperforms the state-of-the-art methods on CityScale dataset with the best topology performance. The result on DeepGlobe dataset improves 4.83% APLS to state-of-the-art methods.