IS-RoadDet: Road Vector Graph Detection With Intersections and Road Segments From High-Resolution Remote Sensing Imagery

Ruoyu Yang, Yanfei Zhong, Yinhe Liu, Dingyuan Chen, Yang Pan

Abstract: Extracting road vector graphs with high accuracy from high-resolution remote sensing imagery presents a significant challenge. The prior end-to-end algorithms have typically modeled the road graph as a general graph structure with vertices and edges, denoted as $G=(V,E)$ , which is a standard approach in graph generation tasks. However, the traditional $G=(V,E)$ graph representation with vertices and edges, utilizing very small edge units, can conflict with the road network’s geometric structure and the inherent features of road instances, leading to issues such as false positives and disconnected roads. In this article, the IS-RoadDet framework is proposed to generate a road vector graph with intersections (I) and road segments (S), denoted as $G=(I,S)$ , which leverages the minimum road topology unit features of road to improve road topology. Compared to $G=(V,E)$ , instead of detecting a large number of vertices to maintain the road topology connectivity, $G=(I,S)$ uses road segments with minimum road units to avoid false positives. In IS-RoadDet, the intersection and road segment detector (ISDetector) is introduced to detect intersections and road segments as independent instance objects with joint learning, and an intersection connectivity strategy (ICS) is designed to establish connectivity between road segments with intersections. Empirical experiments conducted on the SpaceNet3 and Sat2Graph datasets substantiated the superior performance of the proposed road segment modeling method. The code will be made available at: https://github.com/WanderRainy/IS-Road and https://rsidea.whu.edu.cn/is-road.htm.

External IDs:doi:10.1109/tgrs.2024.3483113