综述大模型在多尺度空间智能中的应用，涵盖智能体、智慧城市与地球科学。
A Survey of Large Language Model-Powered Spatial Intelligence Across Scales: Advances in Embodied Agents, Smart Cities, and Earth Science
- 从人类空间认知出发，分析大模型的空间记忆与知识表征机制
- 构建跨尺度框架，贯通从个体导航到全球地球科学的空间推理链路
- 面向跨学科研究者，启发多领域空间智能融合创新
过去一年，大语言模型（LLMs）的发展使空间智能成为关注焦点，尤其集中在基于视觉的具身智能。然而，空间智能涵盖更广泛的学科与尺度，从导航与城市规划到遥感与地球科学。不同领域间的空间智能有何异同？本文首先回顾人类空间认知及其对大模型空间智能的启示；接着探讨大模型中的空间记忆、知识表征与抽象推理，揭示其作用与关联；最后，依据从空间记忆与理解到空间推理与智能的框架，分析跨尺度空间智能——从具身层面到城市及全球层级。本综述旨在为跨学科空间智能研究提供洞见，并激发未来研究。
原文摘要 · Abstract (English)
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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