Geospatial Location Embedding (GLE) helps a Large Language Model (LLM) assimilate and analyze spatial data. GLE emergence in Geospatial Artificial Intelligence (GeoAI) is precipitated by the need for deeper geospatial awareness in our complex contemporary spaces and the success of LLMs in extracting deep meaning in Generative AI. We searched Google Scholar, Science Direct, and arXiv for papers on geospatial location embedding and LLM and reviewed articles focused on gaining deeper spatial "knowing" through LLMs. We screened 304 titles, 30 abstracts, and 18 full-text papers that reveal four GLE themes - Entity Location Embedding (ELE), Document Location Embedding (DLE), Sequence Location Embedding (SLE), and Token Location Embedding (TLE). Synthesis is tabular and narrative, including a dialogic conversation between "Space" and "LLM." Though GLEs aid spatial understanding by superimposing spatial data, they emphasize the need to advance in the intricacies of spatial modalities and generalized reasoning. GLEs signal the need for a Spatial Foundation/Language Model (SLM) that embeds spatial knowing within the model architecture. The SLM framework advances Spatial Artificial Intelligence Systems (SPAIS), establishing a Spatial Vector Space (SVS) that maps to physical space. The resulting spatially imbued Language Model is unique. It simultaneously represents actual space and an AI-capable space, paving the way for AI native geo storage, analysis, and multi-modality as the basis for Spatial Artificial Intelligence Systems (SPAIS).

翻译：地理空间位置嵌入（GLE）有助于大型语言模型（LLM）同化并分析空间数据。GLE在地理空间人工智能（GeoAI）领域的出现，源于当代复杂空间对深层地理空间认知的需求，以及LLM在生成式AI中提取深层含义的成功。我们检索了Google Scholar、Science Direct和arXiv上关于地理空间位置嵌入与LLM的论文，并综述了聚焦于通过LLM获得更深层次空间"认知"的文章。我们筛选了304个标题、30篇摘要和18篇全文，揭示了四个GLE主题——实体位置嵌入（ELE）、文档位置嵌入（DLE）、序列位置嵌入（SLE）和标记位置嵌入（TLE）。综合采用表格和叙述形式呈现，包括"空间"与"LLM"之间的对话式讨论。尽管GLE通过叠加空间数据有助于空间理解，但它们凸显了在空间模态的复杂性和泛化推理方面仍需推进。GLE预示着需要一种将空间认知内嵌于模型架构的空间基础/语言模型（SLM）。SLM框架推动了空间人工智能系统（SPAIS）的发展，建立了一个映射到物理空间的空间向量空间（SVS）。由此产生的具有空间感知的语言模型是独特的。它同时表征实际空间和具备AI能力的空间，为AI原生地理存储、分析和多模态奠定基础，并构成空间人工智能系统（SPAIS）的基础。