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arXiv · 2505.12703

SpatialLLM: From Multi-modality Data to Urban Spatial Intelligence

Jiabin Chen·Haiping Wang·Jinpeng Li·Yuan Liu·Zhen Dong·Bisheng Yang
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Abstract

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 at https://github.com/WHU-USI3DV/SpatialLLM.

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Jiabin Chen, Haiping Wang, Jinpeng Li, Yuan Liu, Zhen Dong, Bisheng Yang. 2025-05-19. SpatialLLM: From Multi-modality Data to Urban Spatial Intelligence. https://arxiv.org/abs/2505.12703

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