RN-Sam: Road Network-Aided Sam Optimization for Road Segmentation In Satellite Imagery

Road segmentation in satellite imagery is critical for various applications, and the Segment Anything Model (SAM) has recently been applied to this task, as with other remote sensing applications. However, despite its advancements, applying SAM to road segmentation poses notable challenges. First, inaccurate prompts can degrade SAM’s performance. Second, existing methods often lack practical evaluation in cross-region scenario. To address these issues, this paper introduces RN-SAM, a novel framework that leverages OpenStreetMap (OSM) as a reliable source of auxiliary road network information to enhance SAM for road segmentation and incorporates a new dataset designed for robust cross-region evaluations. The proposed framework comprises two phases: fine-tuning SAM with road network-based prompts and applying test-time adaptation using OSM road network data. Experimental results on our dataset demonstrate that the proposed framework significantly enhances SAM’s performance for road segmentation and outperforms existing methods in both same-region and cross-region scenarios.