灾后快速滑坡制图对灾害响应至关重要，但由于类别极度不平衡，其自动化仍具挑战性。本研究评估了地理空间基础模型（Geospatial Foundation Model, GFM）Clay v1.5能否提升Landslide4Sense（L4S）基准数据集上的像素级滑坡分割性能；该数据集包含3,799个训练图像块，每个图像块含14个Sentinel-2与地形波段，且正样本像素占比约2%。我们对比了三种策略：以Clay为主编码器并融合多尺度残差地形特征、在U-Net主干网络瓶颈层注入Clay语义上下文，以及标准U-Net基线模型。采用两阶段低秩自适应（Low-Rank Adaptation, LoRA）的混合U-Net + Clay模型在三次随机种子实验中取得最佳测试F1分数64.5 ± 1.8%，优于仅使用Clay作为编码器的模型（55.2 ± 3.6%）和U-Net基线模型（59.9%）。Clay作为独立编码器因缺乏多尺度跳跃连接而表现逊于U-Net，但其预训练表征在作为辅助上下文注入时始终提升模型性能。结果表明，对于滑坡检测任务，地理空间基础模型（GFMs）最有效的应用方式是补充而非替代具有空间细节建模能力的卷积架构。

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.