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.
arXiv:2608. 07411v1 Announce Type: new Abstract: In the context of geodata, existing Large Language Models have often been studied in a homogeneous setting, which has considerably limited insights into their generalization capabilities.
arXiv:2608. 03882v1 Announce Type: cross Abstract: Geospatial reasoning, i.
arXiv:2610.15750v1 Announce Type: new Abstract: Google Earth AI is a family of geospatial AI models, together with an agent that orchestrates them. We present this agent, Google Earth AI's Geospatial...
arXiv:2608. 07353v1 Announce Type: cross Abstract: Understanding concepts is fundamental to generalization.
Geographic Information System (GIS) professionals rely on multi-step spatial analysis workflows to support decision-making in urban planning, disaster response, and environmental monitoring. The process is tedious, time-consuming, and error-prone.
arXiv:2608.30107v1 Announce Type: cross Abstract: Understanding which countries are represented in NLP datasets is essential for identifying gaps, targeting data collection, measuring progress, and i...
arXiv:2606. 13148v1 Announce Type: new Abstract: Climate and environmental decision-making increasingly requires reasoning across heterogeneous inputs, including gridded physical data, satellite imagery, geospatial context, and simulator outputs.
The paper introduces a network‑based spatial context retrieval pipeline that uses pedestrian street networks and open data (OpenStreetMap, GHS‑POP) to generate compact spatial briefs for open‑weight large language models. It then builds a faithfulness benchmark that labels each model claim by its source—whether grounded in the brief or drawn from training knowledge—and tests models against planted false premises across multiple cities and model configurations. The study finds that model family and generation influence resistance to false premises more than model size, revealing dimensions of spatial reasoning not captured by traditional correctness metrics.
arXiv:2609.36082v1 Announce Type: new Abstract: We introduce GeoOutageBench, a benchmark for assessing LLM-based geospatiotemporal KGQA for multimodal outage and resilience analysis. Unlike existing...
Understanding which countries are represented in NLP datasets is essential for identifying gaps, targeting data collection, measuring progress, and informing AI policy. However, geographic metadata is...
arXiv:2606. 12821v1 Announce Type: new Abstract: Environmental scientists spend disproportionate effort on data wrangling rather than analysis, and AI agents that automate geospatial workflows remain unvalidated: no benchmark evaluates agents operating through structured tool calling against real APIs.
arXiv:2607. 06482v1 Announce Type: cross Abstract: Current benchmarks for evaluating Large Language Models (LLMs) in data analysis often fail to reflect real-world settings.