CityLLM enables natural-language queries of semantic 3D city models

Jul 17, 2026

CityLLM is a framework that integrates spatial and graph databases with large language models (LLMs) to allow users to query semantic 3D city models using natural language. In tests on a CityJSON dataset of Rotterdam, CityLLM achieved 85.2–100% answer correctness and 100% query success across 54 queries. The system supports iterative query refinement and chaining across multiple databases, aiming to make complex urban data more accessible to non-experts.

Why it matters: This work could make it easier for a wider range of users to access and analyze complex 3D city data, potentially broadening the use of such models in urban planning and research.

Full story at: arXiv Computation and Language ↗