LoRA-adapted Prithvi-v2 achieves the highest accuracy and best cross-domain generalization for burned-area mapping on Sentinel-2 data compared to full fine-tuning across 3,820 wildfire events.
Unified perceptual parsing for scene understanding
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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%.
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Low-Rank Adaptation of Geospatial Foundation Models for Wildfire Mapping Using Sentinel-2 Data
LoRA-adapted Prithvi-v2 achieves the highest accuracy and best cross-domain generalization for burned-area mapping on Sentinel-2 data compared to full fine-tuning across 3,820 wildfire events.
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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%.