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