Abstract: Automatic coastline extraction from remote sensing imagery is of great significance for the monitoring, assessment, and management of coastal zone environmental resources. However, achieving high-precision and strongly generalizable sea-land segmentation still faces many challenges, as it is influenced by the complexity of coastal terrain structures, significant differences in spatial scales, and the ambiguity of boundaries. To address the above issues, the proposed sea-land segmentation method, named VMA-Net, uses a visual state space model as the encoder for accurate modeling of long-range spatial dependencies in remote sensing images, and incorporates the ASPP (atrous spatial pyramid pooling) module and the CBAM (convolutional block attention module), which collaboratively enhance the perception of multi-scale contextual information and the representation of key regions. Extensive experimental results on three remote sensing coastline datasets—Benchmark Sea-land Dataset, GF-HNCD, and sea-land segmentation V1.1—demonstrate that the proposed VMA-Net outperforms various mainstream deep learning methods in terms of quantitative metrics such as mF1 and MIoU. Specifically, on the BSD, GF-HNCD, and sea-land segmentation V1.1 datasets, the mF1 scores reach 98.35%, 98.38%, and 99.26%, respectively, while the MIoU scores are 96.75%, 96.81%, and 98.53%, respectively. Meanwhile, with 35.46 million parameters and 25.41 GFLOPs, the model achieves a good balance between accuracy and efficiency, providing strong technical support for intelligent monitoring and scientific management of coastal zone environmental resources

Key words: Vision Mamba, Attention Mechanism, Remote Sensing Imagery, Coastal Resource Monitoring, Sea-Land Segmentation