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Geospatial foundation models for disaster assessment exhibit severe shortcut learning, with performance plummeting from 0.80+ to 0.63 accuracy when evaluated on unseen disaster types, and simple building counts alone achieve comparable performance.
Uncertainty-aware models can significantly enhance the reliability of EO regression tasks, outperforming existing benchmarks and providing actionable confidence estimates.
Uncertainty-aware retrieval can significantly enhance the reliability of cross-modal remote sensing systems, even under challenging conditions.
Forget full fine-tuning: LoRA lets you adapt Geospatial Foundation Models for wildfire mapping with comparable accuracy while only tweaking 1% of the parameters.
Ditch the costly sampling: Noise2Map turns diffusion models into fast, end-to-end semantic segmentation and change detection machines by directly predicting maps from noise.
