EORENet: Edge-Optimized Road Extraction from 2 m GF-1 Imagery for Large-Area Road Mapping

Accurate road extraction from high-resolution satellite imagery is important for transportation planning, infrastructure monitoring, and geospatial updating, but blurred boundaries and fragmented segments still limit practical large-area applications. This study proposes EORENet, an edge-optimized road extraction network for 2 m GF-1 imagery and large-area road product generation. EORENet decouples road-surface segmentation and road-edge delineation into task-specific branches, introduces deformable convolution for adaptive spatial aggregation, and uses a dual-task cross-feature fusion module to exchange semantic and boundary cues. A GF-1 road sample set, the Cangling Road Dataset, was constructed and evaluated together with the Ottawa and DeepGlobe Road datasets. Compared with eight representative methods, EORENet achieved the best overall IoU, F1 score, and edge-buffered F1 score; on the Cangling dataset, it reached 55.11% IoU, 71.06% F1, and 56.21% edge-buffered F1. The trained model was then applied to a spatially independent large-area GF-1 inference collection. Scene-stratified product-level assessment with 1200 independently annotated 512 × 512 patches showed systematic scene-dependent differences, with F1 scores ranging from 62.80% in mountainous forest areas to 74.57% in high-density urban cores and a balanced scene-wise mean F1 of 69.48%. City-scale extraction over four metropolitan regions further visually illustrated the workflow’s potential for large-area road-surface product generation.