LAUSR

Road extraction from very high-resolution (VHR) remote sensing imagery remains a huge challenge, due to the shadows and occlusions of trees and buildings. Such complex backgrounds result in deep networks often producing fragmented roads with poor connectivity. Road extraction has three typical tasks: road surface segmentation (SS), centerline extraction (CE), and edge detection (ED), which are conducted in a wide range of real applications. Also, the three tasks have a symbiotic relationship, i.e., the road SS determines the location of the centerline and edges, and the CE and ED can allow the generation of more continuous road surfaces. However, most of the previous works have completed these three tasks separately, without exploiting the symbiotic relationship between them to boost the road connectivity. In this article, in order to improve road connectivity, a cascaded multitask (CasMT) road extraction framework for simultaneously extracting the road surface, centerline, and edges is proposed. In the proposed framework, topology-aware learning is applied to capture the long-distance topological relationships, and hard example mining (HEM) loss is employed to focus more on hard samples, to further enhance the road completeness. Extensive experiments were conducted on the DeepGlobe road dataset and a large-scale road dataset (called the LSCC dataset) from the three Chinese cities of Beijing, Shanghai, and Wuhan. The experimental results obtained on the public DeepGlobe dataset demonstrate that the proposed CasMT framework can significantly outperform the current state-of-the-art method. Moreover, the generalization capability of the model was verified on the LSCC dataset, where the proposed CasMT framework achieved the best performance in the average path length similarity (APLS) road topology metric, which further confirms the superiority of the proposed framework.