Road Extraction via the Complementary Relationship Between Architectural and Nonarchitectural Areas

Road extraction from remote sensing images is crucial for applications such as autonomous driving and urban planning. However, existing methods often neglect the spatial correlation between roads and adjacent buildings, limiting extraction accuracy. This study proposes SAM BE Road, a road-building co-extraction framework based on the Segment Anything Model (SAM), which optimizes road extraction performance by integrating building spatial information. Extensive experiments are carried out on the Massachusetts dataset and the New York dataset, and the results demonstrate that our SAM BE Road outperforms other state-of-the-art methods in extraction accuracy and topological connectivity. The road labels extracted by our method exhibit preferable connectivity, especially in complex urban environments.