Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Geospatial Reasoning Questions

TL;DR

Spatial-RAG is a novel framework that combines spatial databases and large language models to improve geospatial question answering by reasoning over geographic relationships and user intent.

Contribution

It introduces a hybrid spatial retriever and multi-objective optimization approach to enhance LLM-based geospatial reasoning and question answering capabilities.

Findings

Significantly improves accuracy over baselines

Enhances precision and ranking performance

Effective across multiple tourism and map-based datasets

Abstract

Answering real-world geospatial questions--such as finding restaurants along a travel route or amenities near a landmark--requires reasoning over both geographic relationships and semantic user intent. However, existing large language models (LLMs) lack spatial computing capabilities and access to up-to-date, ubiquitous real-world geospatial data, while traditional geospatial systems fall short in interpreting natural language. To bridge this gap, we introduce Spatial-RAG, a Retrieval-Augmented Generation (RAG) framework designed for geospatial question answering. Spatial-RAG integrates structured spatial databases with LLMs via a hybrid spatial retriever that combines sparse spatial filtering and dense semantic matching. It formulates the answering process as a multi-objective optimization over spatial and semantic relevance, identifying Pareto-optimal candidates and dynamically selecting the best response based on user intent. Experiments across multiple tourism and map-based QA datasets show that Spatial-RAG significantly improves accuracy, precision, and ranking performance over strong baselines.