GeoPhysAdapter：用于基于视觉基础模型的跨域滑坡制图的尺度匹配地球物理适配方法
GeoPhysAdapter: Scale-Matched Geophysical Adaptation for Cross-Domain Landslide Mapping with Vision Foundation Models
- Institute of Space and Earth Information Science, The Chinese University of Hong Kong（香港中文大学太空与地球信息科学研究所）
- Department of Geography and Resource Management, The Chinese University of Hong Kong（香港中文大学地理与资源管理学系）
- Urban Governance and Design Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）城市治理与设计学域）
- Department of Geography, National University of Singapore（新加坡国立大学地理学系）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对跨域滑坡制图中视觉基础模型的误报问题，提出GeoPhysAdapter方法，在像素和候选滑坡体决策单元结合地球物理约束适配，在PILD数据集上大幅降低了假阳性误差。
AI中文摘要：
新触发的滑坡很少带有即时标注，因此跨域可迁移性决定了滑坡制图在应急响应和区域风险评估中的价值。视觉基础模型已增强了表征迁移能力，但在未见过的区域、事件和数据源上仍会产生高置信度的误报。地形、物质和降雨触发因素可约束此类误差，但它们的支持范围分别为局部、区域和事件尺度，因此重采样到10米网格会使其与分割决策单元错位，加剧不确定地理背景问题（UGCoP）。我们提出GeoPhysAdapter，它以冻结的视觉基础模型为基础，将地形、物质和触发因素分别限定为密集空间引导、区域调制和事件时间强制，并在像素和候选滑坡体两个决策单元应用有界适配，在支持不足时完全恢复为视觉预测。在包含四个公共来源、55个全球滑坡事件和7890个测试样本的事件隔离PILD数据集上，70.3%的跨域假阳性质量位于等效中值直径207米的近纯虚假体中，与粗先验支持而非像素匹配。像素级适配净移除507817个错误像素，误差降低7.76%；而在相同样本、锚点和基线条件下，将决策单元提升至候选体，误差降低幅度增至23.99%，约为像素级效果的3.1倍，IoU提升0.031（相对提升14.2%），每受影响像素校正9.92个像素。数据和代码可在this httpsURL公开获取。
英文摘要：
Newly triggered landslides rarely carry immediate annotations, so cross-domain transferability determines the value of landslide mapping for emergency response and regional risk assessment. Vision foundation models have strengthened representational transfer, yet on unseen regions, events, and data sources they still generate high-confidence false alarms. Terrain, material, and rainfall triggering can constrain such errors, but their supports are local, regional, and event-scale, so that resampling onto a 10~m grid misaligns them with the segmentation decision unit and compounds the uncertain geographic context problem (UGCoP). We propose GeoPhysAdapter, which anchors on a frozen vision foundation model, restricts terrain, material, and triggering to dense spatial guidance, regional modulation, and event-timing forcing, and applies bounded adaptation at two decision units, the pixel and the candidate landslide body, reverting exactly to the visual prediction where support is insufficient. On an event-isolated PILD dataset of four public sources, 55 global landslide events, and 7,890 test samples, 70.3% of cross-domain false-positive mass lies in near-pure spurious bodies of median equivalent diameter 207m, matching coarse-prior support rather than the pixel. Pixel-level adaptation removes a net 507,817 erroneous pixels and reduces error by 7.76%, whereas raising the decision unit to the candidate body, under identical samples, anchor, and baseline, increases error reduction to 23.99%, approximately 3.1 times the pixel-level effect, improves IoU by 0.031 (14.2% relative), and corrects 9.92 pixels per pixel harmed. The data and code are publicly available at: https://github.com/Liu-Zhihang/geophysadapter.
