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A Survey of Large Language Model-Powered Spatial Intelligence Across Scales: Advances in Embodied Agents, Smart Cities, and Earth Science

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arxiv 2504.09848 v1 pith:SSGFBWGY submitted 2025-04-14 cs.AI cs.CL

Jie Feng , Jinwei Zeng , Qingyue Long , Hongyi Chen , Jie Zhao , Yanxin Xi , Zhilun Zhou , Yuan Yuan
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Shengyuan Wang Qingbin Zeng Songwei Li Yunke Zhang Yuming Lin Tong Li Jingtao Ding Chen Gao Fengli Xu Yong Li
This is my paper · ORCID
classification cs.AIcs.CL
keywords spatialintelligenceacrossembodiedllmsscalesconnectionsearth
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Over the past year, the development of large language models (LLMs) has brought spatial intelligence into focus, with much attention on vision-based embodied intelligence. However, spatial intelligence spans a broader range of disciplines and scales, from navigation and urban planning to remote sensing and earth science. What are the differences and connections between spatial intelligence across these fields? In this paper, we first review human spatial cognition and its implications for spatial intelligence in LLMs. We then examine spatial memory, knowledge representations, and abstract reasoning in LLMs, highlighting their roles and connections. Finally, we analyze spatial intelligence across scales -- from embodied to urban and global levels -- following a framework that progresses from spatial memory and understanding to spatial reasoning and intelligence. Through this survey, we aim to provide insights into interdisciplinary spatial intelligence research and inspire future studies.

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Cited by 2 Pith papers

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  1. UrbanLLaVA: A Multi-modal Large Language Model for Urban Intelligence with Spatial Reasoning and Understanding

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A fine-tuned small multimodal LLM outperforms much larger general models on urban tasks in a new benchmark, with caveats about benchmark overlap with training data.

  2. Open-Set Living Need Prediction with Large Language Models

    cs.AI 2025-06 conditional novelty 6.0 of 10

    PIGEON uses LLMs with retrieved user history and Maslow's hierarchy to predict open-set living needs in free text, improving life service recall over closed-set baselines.

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