An audit of 152 papers reveals that geospatial foundation models lack standardized evaluations, training controls, and weight releases, so no one knows the state of the art.
Geo- bench-2: From performance to capability, rethinking evaluation in geospatial ai
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CV 2years
2026 2representative citing papers
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.
citing papers explorer
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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.