SpatialLLM: From Multi-modality Data to Urban Spatial Intelligence

We propose SpatialLLM, a novel approach advancing spatial intelligence tasks in complex urban scenes. Unlike previous methods requiring geographic analysis tools or domain expertise, SpatialLLM is a unified language model directly addressing various spatial intelligence tasks without any training, fine-tuning, or expert intervention. The core of SpatialLLM lies in constructing detailed and structured scene descriptions from raw spatial data to prompt pre-trained LLMs for scene-based analysis. Extensive experiments show that, with our designs, pretrained LLMs can accurately perceive spatial distribution information and enable zero-shot execution of advanced spatial intelligence tasks, including urban planning, ecological analysis, traffic management, etc. We argue that multi-field knowledge, context length, and reasoning ability are key factors influencing LLM performances in urban analysis. We hope that SpatialLLM will provide a novel viable perspective for urban intelligent analysis and management. The code and dataset are available atthis https URL.

@article{chen2025_2505.12703,
  title={ SpatialLLM: From Multi-modality Data to Urban Spatial Intelligence },
  author={ Jiabin Chen and Haiping Wang and Jinpeng Li and Yuan Liu and Zhen Dong and Bisheng Yang },
  journal={arXiv preprint arXiv:2505.12703},
  year={ 2025 }
}