LAUSR

Alluvial fans are crucial geomorphic features in arid regions, playing key roles in geomorphic evolution, hydrological modeling, and land-use planning. However, their irregular morphology and multi-scale characteristics make accurate boundary delineation challenging for conventional remote sensing methods.To overcome these limitations, this study proposes a multi-module enhanced Mask R-CNN framework that integrates topographic and spectral information for precise alluvial fan recognition. The model consists of a Topographic–Spectral Fusion (TSF) module, a Scale-Adaptive Module (SAM), and a Mask–Boundary Refinement (MBR) module, jointly designed to improve recognition accuracy and structural detail preservation.Experiments based on multi-source remote sensing imagery and terrain data show that the proposed model achieves an accuracy of 91.7%, precision of 89.8%, recall of 88.5%, and F1-score of 89.1% in full-region classification. For segmentation, the model attains a mean intersection over union (mIoU) of 81.5% and a boundary F1-score of 80.4%. Ablation experiments confirm that the TSF module enhances spatial–structural modeling, while the MBR module improves boundary fitting.The results demonstrate that the proposed framework provides robust and transferable performance across different fan size categories, achieving a minimum false negative rate of 3.9%. The method offers both theoretical value and practical applicability for accurate alluvial fan recognition in arid regions.