Clay-CNN混合模型：利用地理基础模型作为滑坡检测的辅助上下文
Clay-CNN Hybrids: Leveraging Geospatial Foundation Models as Auxiliary Context for Landslide Detection
AI总结：
针对滑坡检测中的极端类别不平衡问题，提出将地理基础模型Clay v1.5作为辅助上下文注入U-Net瓶颈的混合方法，在Landslide4Sense基准上达到64.5% F1，优于纯Clay或U-Net基线。
摘要：
灾后快速滑坡制图对灾害响应至关重要，但由于极端类别不平衡，自动化仍然困难。本研究评估了地理基础模型（GFM）Clay v1.5是否能够改善Landslide4Sense（L4S）基准上的像素级滑坡分割，该基准包含3,799个训练块，具有14个Sentinel-2和地形波段，约2%的正像素。我们比较了三种策略：Clay作为主编码器并融合多尺度残差地形、在瓶颈处注入Clay语义上下文的U-Net骨干、以及标准U-Net基线。采用两阶段低秩适应（LoRA）的混合U-Net + Clay模型在三个随机种子上的最佳测试F1为64.5±1.8%，超过了纯Clay骨干（55.2±3.6%）和U-Net基线（59.9%）。由于缺乏多尺度跳跃连接，Clay作为独立编码器的性能低于U-Net，但其预训练表示在作为辅助上下文注入时持续提升了性能。这些发现表明，GFM在滑坡检测中最有效的方式是补充空间细节丰富的卷积架构，而非替代它们。
英文摘要：
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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