Spliting Road Network to Road Segment Instances for Vector Road Mapping

Extracting road network structures with high accuracy from very high-resolution (VHR) remote sensing imagery is a challenging task in the field of computer vision. Previous end-to-end algorithms modeled the road graph as a general graph structure with defined vertices and edges, represented as G = (V, E), which is a default approach in graph generation tasks. However, the use of excessively small edge units conflicts with the network’s geometric structure and potential object features of roads, leading to issues such as false alarm roads and disconnected roads. In this study, we propose a road graph generation model based on road segments, represented as G = (I, S), leveraging the long-distance connectivity of minimal road topological units. This approach aims to balance the connectivity and precision of the generated road graph. Subsequently, we introduce a road graph generation framework named RoadSegment, based on the aforementioned modeling, to validate the feasibility of the proposed road segment modeling. RoadSegment utilizes an Element Detector to identify intersections and road segment objects. Additionally, we propose an Intersection Connection Strategy (ICS) to establish connectivity between road segments. Empirical experiments conducted on the SpaceNet3 Dataset demonstrate the superiority of road segment modeling.