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

TL;DR

This paper systematically reviews geospatial location embedding approaches in large language models, highlighting their role in advancing spatial AI systems through a new framework called Spatial Foundation/Language Model.

Contribution

It introduces a comprehensive review of GLE themes and proposes the SLM framework to embed spatial knowledge within language models for enhanced spatial reasoning.

Findings

Identified four GLE themes: ELE, DLE, SLE, TLE.

Highlighted the need for a Spatial Foundation/Language Model.

Proposed the SPAIS framework with a Spatial Vector Space.

Abstract

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).