RAP-Net: A Region Affinity Propagation-Guided Semantic Segmentation Network for Plateau Karst Landform Remote Sensing Imagery

Karst rocky desertification on the Qinghai–Tibet Plateau poses a severe threat to the region’s fragile ecosystem. Accordingly, the rapid and accurate delineation of plateau karst landforms is essential for monitoring ecological degradation and guiding restoration strategies. However, automatic recognition of these landforms in remote sensing imagery is hindered by challenges such as blurred boundaries, fragmented targets, and poor intra-region consistency. To address these issues, we propose the Region Affinity Propagation Network (RAP-Net). This framework enhances intra-region consistency, edge sensitivity, and multi-scale context fusion through its core modules: Region Affinity Propagation (RAP), High-Frequency Multi-Scale Attention (HFMSA), and Global–Local Cross Attention (GLCA). In addition, we constructed the Plateau Karst Landform Dataset (PKLD), a high-resolution remote sensing dataset specifically tailored for this task, which provides a standardized benchmark for future studies. On the PKLD, RAP-Net surpasses eight state-of-the-art methods, achieving 3.69–10.31% higher IoU and 3.88–14.28% higher Recall, thereby demonstrating significant improvements in boundary delineation and structural completeness. Moreover, in a cross-regional generalization test on the Mount Genyen area, RAP-Net—trained solely on PKLD without fine-tuning—achieved 2.38% and 1.94% higher IoU and F1-scores, respectively, than the Swin Transformer, confirming its robustness and generalizability in complex, unseen environments.