UBR-Net: Road Extraction from High-Resolution Remote Sensing Imagery Using Multi-Scale Attention and Cross-Residual Encoding

Extracting road features from high-resolution remote sensing imagery is crucial for urban planning, navigation systems. However, this task is challenged by factors such as occlusions from buildings, interruptions in road continuity, and variations in road width and appearance. These issues often lead to segmentation discontinuities and misclassifications. This paper presents the Urban Road Extraction Network (UBR-Net), an architecture that enhances the DeepLabv3+ model to address these challenges. The key contributions lie in the specific design and integration of several modules. We introduce Cross-Residual Encoding blocks designed to preserve fine-grained details and mitigate the gradient vanishing problem, thereby improving road continuity. Additionally, UBR-Net incorporates a Multiscale Context Features Extraction (MCFE) module, enhanced with an Improved Self-Attention Block (ISAB), to capture rich, hierarchical feature representations with a focus on long-range dependencies. A Channel Spatial Attention Module (CSAM) is also integrated to refine the feature extraction process by focusing on critical channels and spatial regions. Evaluations on public datasets, including DeepGlobe and Massachusetts, show that UBR-Net reduces extraction errors from occlusions, achieving a 75.18% F1 score and a 57.22% Intersection over Union (IoU), surpassing existing methods. These results highlight UBR-Net’s effectiveness for urban analysis and its potential for more precise and efficient urban planning.