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September 17, 2025

Large Language Models for Spatial Analysis Queries

Key Points

Employing LLMs as is improves efficiency in spatial analysis tasks, and allows for quicker results.
Fine-tuning LLMs for specific spatial queries can significantly enhance accuracy and relevance of responses.
Retaining and modifying LLM architectures for spatial contexts opens new avenues for understanding complex queries.
This tutorial outlines benchmarks and identifies research gaps in the usage of LLMs for spatial analysis.

Abstract

This tutorial provides a comprehensive overview of the research landscape of employing Large Language Models (LLMs) to spatial analysis queries. The tutorial categorizes the research in this area based on how LLMs are employed to serve such queries. This goes from employing LLMs as is, to fine-tuning LLMs, to completely retrain LLM architectures, to modifying the LLM internals to fit spatial queries. The tutorial concludes by a set of benchmarks and pointing out to research gaps and future research directions.

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