大模型驱动空间智能综述：具身智能体、智慧城市与地球科学的进展

Core Viewpoint - The article discusses the evolution of spatial intelligence through the development of large language models (LLMs) and their applications across various scales, from micro-level robotics to macro-level earth sciences, highlighting both opportunities and challenges in this field [1][2][35]. Section Summaries Section 1: The Foundation of Spatial Intelligence - How Large Models "Understand" Space - To enable machines to possess spatial intelligence, they must develop effective spatial memory and flexible abstract spatial reasoning capabilities [2][3]. Section 2: Spatial Memory and Knowledge - The "Cognitive Map" in Large Models - Large models acquire spatial information through "internal absorption" during pre-training and "external invocation" when needing real-time data [4][5]. Section 3: Abstract Spatial Reasoning - Beyond Memorization - Current large models primarily mimic spatial tasks using language modeling rather than possessing deep spatial reasoning akin to human cognition [9]. Section 4: Multi-Scale Spatial Intelligence Applications Driven by Large Models - Large models are increasingly important in various spatial intelligence tasks across different scales, from individual robots to urban environments and global systems [10][11]. Section 5: Embodied Intelligence - Enhancing Robot Spatial Understanding and Action - The development of embodied intelligence focuses on enabling robots to perceive, understand, and navigate physical environments effectively [11][12]. Section 6: Urban Spatial Intelligence - Empowering Smarter, More Livable Cities - Large models are applied in urban settings to enhance spatial understanding, reasoning, and decision-making for better city management [15][16]. Section 7: Earth Spatial Intelligence (ESI) - Insights into Our Planet - ESI leverages AI and large models to analyze vast amounts of earth observation data, addressing global challenges like climate change and resource management [20][21]. Section 8: Challenges and Prospects - The Future of Spatial Intelligence - Despite significant advancements, challenges remain in spatial reasoning, data integration, and model interpretability, necessitating ongoing research and development [29][30].