Transformer-based models deliver strong landslide segmentation on satellite images, and parameter-efficient fine-tuning matches full fine-tuning accuracy while cutting trainable parameters by up to 95%.
Landslide hazard mapping with geospatial foundation models: Geographical generalizability, data scarcity, and band adaptability
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A Benchmark Study of Segmentation Models and Adaptation Strategies for Landslide Detection from Satellite Imagery
Transformer-based models deliver strong landslide segmentation on satellite images, and parameter-efficient fine-tuning matches full fine-tuning accuracy while cutting trainable parameters by up to 95%.