SSCNet: Synchronous Stratification and Cross-Level Feature Fusion for Road Extraction

Deep learning-based road extraction technologies can swiftly identify road information in complex environments, playing a crucial role in advancing urban intelligence and achieving sustainable development goals. However, the current road extraction models exhibit significant omissions when confronted with scenarios involving occluded roads and densely distributed road networks. To address the issue of partial road extraction, this paper introduces a novel method named SSCNet. This approach addresses potential causes of road extraction failures by leveraging synchronous stratification learning and cross-level feature fusion to fully utilize information across various layers. The model incorporates a Dynamic Road Detail Matcher to extract a wealth of road detail information from shallow feature maps, a Cross Contextual Adaptive Attention to capture multi-scale contextual information and interact with shallow feature maps, and a Multi-Scale Global Information Integrator to consolidate global information from different levels of feature maps, enhancing the model’s understanding of road integrity. The model has been extensively tested on the public datasets DeepGlobe, Massachusetts, SpaceNet, and RoadTracer, showing significant improvements in F1 score and IoU compared to current state-of-the-art models.