GLP-Mamba: A Global-Local Perception Mamba Model for Road Extraction from Optical Remote Sensing Imagery

Road extraction from high-resolution remote sensing imagery remains an active research topic in the remote sensing and intelligent transportation communities. Despite recent advances, existing methods still face significant challenges, including fragmented road representations and inaccurate boundary delineation. This paper proposes a road extraction model, termed GLP-Mamba, built upon the RS-Mamba architecture. The proposed model introduces a Global–Local Perception (GLP) module and a Structural Consistency Enhancement (SCE) Block. GLP refines shallow feature representations before fusion, while SCE performs residual structural refinement during decoding. Together, they improve semantic discrimination, boundary representation, and structural continuity. Experiments are conducted on three road extraction datasets: the DeepGlobe Roads Dataset, the Massachusetts Roads Dataset, and the Jilin-1 Satellite Dataset. The results show that GLP-Mamba achieves the highest F1 score and Intersection over Union (IoU) among the compared methods. Ablation studies further verify the effectiveness of each component. Specifically, the OSS Block provides long-range contextual modeling, the GLP module strengthens shallow feature representations before fusion, and the Structural Consistency Enhancement Block helps refine road boundaries and reduce fragmented predictions. These results indicate that GLP-Mamba provides an effective solution for fine-grained road segmentation in complex remote sensing scenarios.