arXiv AI
Google Earth AI Geospatial Reasoning Agent: Capabilities and a Systematic Evaluation
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Earth-Agent-Pro: Towards Real-World Full-Chain Earth Observation with Agents
arXiv:2609.12533v1 Announce Type: cross Abstract: Real-world Earth observation (EO) agents must translate high-level scientific questions into executable workflows to acquire observations, prepare da...
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TerraBench: Can Agents Reason Over Heterogeneous Earth-System Data?
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GISAgentBench: A Practitioner-Sourced Benchmark for Evaluating LLM Agents on GIS Tasks
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GeoFitBench-LLM: A Benchmark for Assessing Large Language Models' Capabilities in Geospatial Data Requirements Elicitation for Geo-Analytic Tasks
GeoFitBench-LLM is a benchmark designed to evaluate large language models (LLMs) on their ability to elicit geospatial data requirements for geo‑analytic tasks. It focuses on two reasoning dimensions—spatio‑temporal relational and semantic grounding—and uses a provider‑independent schema to encode ground‑truth requirements. Experiments on seven models show that while many can identify necessary resources, accurately specifying fitness‑for‑use remains challenging, with GPT‑5.4 Thinking recovering all resources in 66.67% of cases.