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Geospatial Location Embedding (GLE) helps a Large Language Model (LLM)assimilate and analyze spatial data. GLE emergence in Geospatial ArtificialIntelligence (GeoAI) is precipitated by the need for deeper geospatialawareness in our complex contemporary spaces and the success of LLMs inextracting deep meaning in Generative AI. We searched Google Scholar, ScienceDirect, and arXiv for papers on geospatial location embedding and LLM andreviewed articles focused on gaining deeper spatial "knowing" through LLMs. Wescreened 304 titles, 30 abstracts, and 18 full-text papers that reveal four GLEthemes - 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 superimposingspatial data, they emphasize the need to advance in the intricacies of spatialmodalities and generalized reasoning. GLEs signal the need for a SpatialFoundation/Language Model (SLM) that embeds spatial knowing within the modelarchitecture. The SLM framework advances Spatial Artificial IntelligenceSystems (SPAIS), establishing a Spatial Vector Space (SVS) that maps tophysical space. The resulting spatially imbued Language Model is unique. Itsimultaneously represents actual space and an AI-capable space, paving the wayfor AI native geo storage, analysis, and multi-modality as the basis forSpatial Artificial Intelligence Systems (SPAIS).

A systematic review of geospatial location embedding approaches in large language models: A path to spatial AI systems