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%).
Assessing the value of geo-foundational models for flood inundation mapping: Benchmarking models for Sentinel-1, Sentinel- 2, and PlanetScope for end-users,
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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%).