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 capa- bility, rethinking evaluation in geospatial ai
2 Pith papers cite this work. Polarity classification is still indexing.
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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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No One Knows the State of the Art in Geospatial Foundation Models
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.
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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.