IMAGEO-Bench evaluates 10 LLMs on image geolocalization across global street scenes, US POIs, and private images, revealing closed-source model advantages and biases favoring high-resource regions.
International Journal of Disaster Risk Reduction, 98:104062
2 Pith papers cite this work. Polarity classification is still indexing.
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2025 2verdicts
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Introduces Geospatial Awareness Layer (GAL) to ground LLM agents in structured earth data for evidence-based resource allocation in wildfire response.
citing papers explorer
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From Pixels to Places: A Systematic Benchmark for Evaluating Image Geolocalization Ability in Large Language Models
IMAGEO-Bench evaluates 10 LLMs on image geolocalization across global street scenes, US POIs, and private images, revealing closed-source model advantages and biases favoring high-resource regions.
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Empowering LLM Agents with Geospatial Awareness: Toward Grounded Reasoning for Wildfire Response
Introduces Geospatial Awareness Layer (GAL) to ground LLM agents in structured earth data for evidence-based resource allocation in wildfire response.