GeoLinkNet: A Geometry-Aware Deep Learning Model for Road Extraction From Satellite Imagery

Road extraction from satellite imagery is vital for urban planning, transportation, and disaster response. While deep learning models like U-Net, LinkNet34, D-LinkNet, MACU-Net, and RCFSNet have advanced this field, they still struggle with road connectivity, multi-scale feature capture, and complex scenes. To address these challenges, we introduce GeoLinkNet, a novel deep learning architecture for improved road segmentation. GeoLinkNet features four innovations: local context attention (LCA) to enhance spatial coherence; geometric feature extractor (GFE) using directional convolutions for better structural recognition; adaptive fusion module (AFM) for effective multi-level feature integration; and residual refinement module (RRM) to sharpen segmentation with residual learning. Experiments on the DeepGlobe road extraction dataset show that GeoLinkNet outperforms existing methods, achieving better road continuity, segmentation accuracy, and feature representation. The model successfully balances fine detail preservation with global structural awareness, offering a scalable and robust solution for satellite-based road extraction.