用地理基础模型提升滑坡检测，效果优于单独使用或简单替换传统模型。
Clay-CNN Hybrids: Leveraging Geospatial Foundation Models as Auxiliary Context for Landslide Detection
- 将地理基础模型作为辅助上下文注入U-Net瓶颈层
- 两阶段低秩适配后达到64.5%测试F1，显著优于基线和纯模型
- 适合需要高精度遥感灾害监测的研究与应急响应应用
快速灾后滑坡制图对灾害响应至关重要，但因类别极度不平衡而难以自动化。本研究评估了地理基础模型Clay v1.5在Landslide4Sense（L4S）基准上的表现，该数据集包含3,799个训练样本，14个哨兵2号与地形波段，正样本占比约2%。比较三种策略：以Clay为唯一编码器并融合多尺度地形特征、在U-Net瓶颈层注入Clay语义上下文的混合模型，以及标准U-Net基线。采用两阶段低秩适配（LoRA）的混合模型取得最佳测试F1为64.5 ± 1.8%（三组随机种子），超越仅用Clay编码器（55.2 ± 3.6%）和基线U-Net（59.9%）。Clay作为独立编码器表现低于U-Net，因其缺乏多尺度跳跃连接；但其预训练表示作为辅助上下文时可稳定提升性能。结果表明，地理基础模型在与空间细节丰富的卷积架构互补时，对滑坡检测最为有效。
原文摘要 · Abstract (English)
Rapid post-event landslide mapping is essential for disaster response but remains difficult to automate due to extreme class imbalance. This study evaluates whether Clay v1.5, a Geospatial Foundation Model (GFM), can improve pixel-level landslide segmentation on the Landslide4Sense (L4S) benchmark, which contains 3,799 training chips with 14 Sentinel-2 and terrain bands and approximately 2% positive pixels. We compare three strategies: Clay as the primary encoder with multi-scale residual terrain fusion, a U-Net backbone augmented with Clay semantic context at the bottleneck, and a standard U-Net baseline. The hybrid U-Net + Clay model with two-stage Low-Rank Adaptation (LoRA) achieved the best test F1 of 64.5 +/- 1.8% over three seeds, surpassing the Clay-only backbone (55.2 +/- 3.6%) and the U-Net baseline (59.9%). Clay as a standalone encoder underperformed the U-Net due to the absence of multi-scale skip connections, but its pretrained representations consistently improved performance when injected as auxiliary context. These findings suggest that GFMs are most effective for landslide detection when they complement spatially detailed convolutional architectures rather than replace them.
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