Benchmarking LLMs for Geospatial Intelligence
Evaluating AI models on multi-step GIS tasks for real-world applications
This research establishes the first benchmark specifically designed to evaluate how LLMs perform on complex, multi-step geospatial tasks that GIS professionals encounter in commercial settings.
- Tests 7 leading commercial LLMs (including Sonnet, Gemini, and GPT models) using a tool-calling agent with 23 geospatial functions
- Evaluates performance across four categories of increasing complexity, including intentionally unsolvable tasks
- Reveals significant performance gaps between models in handling geospatial reasoning
- Provides insights for engineering organizations on which AI models are most reliable for GIS applications
For engineering teams building location-based solutions, this benchmark offers crucial data on which LLMs can effectively handle spatial analysis without hallucinating capabilities.