GeoNatureAgent Benchmark tests seven LLMs on 93 tasks via a production geospatial API, with Claude Sonnet 4 at 60.8% and DeepSeek V3.2 offering near performance at 11x lower cost while all models fail on close-value comparisons.
Geo- bench-2: From performance to capability, rethinking evaluation in geospatial ai
5 Pith papers cite this work. Polarity classification is still indexing.
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DeluluNet enables continued prediction under modality substitution, addition, or subsets by training a multi-modal model from a unimodal teacher and unlabeled multimodal data via modality hallucination.
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.
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.
Remote-sensing foundation models need domain-specific design and evaluation around measurement physics and decision constraints; benchmark accuracy alone is insufficient for trustworthy EO deployment.
citing papers explorer
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GeoNatureAgent Benchmark: Benchmarking LLM Agents for Environmental Geospatial Analysis Across Frontier and Open-Weight Foundation Models
GeoNatureAgent Benchmark tests seven LLMs on 93 tasks via a production geospatial API, with Claude Sonnet 4 at 60.8% and DeepSeek V3.2 offering near performance at 11x lower cost while all models fail on close-value comparisons.
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Changing Modalities: Adapting Remote Sensing Models to New Satellites and Sensors
DeluluNet enables continued prediction under modality substitution, addition, or subsets by training a multi-modal model from a unimodal teacher and unlabeled multimodal data via modality hallucination.
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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.
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Scalable and Trustworthy Earth Observation Foundation Models
Remote-sensing foundation models need domain-specific design and evaluation around measurement physics and decision constraints; benchmark accuracy alone is insufficient for trustworthy EO deployment.