Hybrid U-Net augmented with Clay GFM context via two-stage LoRA reaches 64.5% test F1 on Landslide4Sense, beating both standalone Clay (55.2%) and plain U-Net (59.9%).
Brief communication: AI-driven rapid landslide mapping following the 2024 Hualien earthquake in Taiwan,
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Clay-CNN Hybrids: Leveraging Geospatial Foundation Models as Auxiliary Context for Landslide Detection
Hybrid U-Net augmented with Clay GFM context via two-stage LoRA reaches 64.5% test F1 on Landslide4Sense, beating both standalone Clay (55.2%) and plain U-Net (59.9%).