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

• URL: http://arxiv.org/abs/2502.18470v5
• Date: Wed, 11 Jun 2025 04:41:29 GMT
• Title: Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Geospatial Reasoning Questions
• Authors: Dazhou Yu, Riyang Bao, Ruiyu Ning, Jinghong Peng, Gengchen Mai, Liang Zhao,
• Abstract summary: Spatial-RAG is a Retrieval-Augmented Generation framework designed for geospatial question answering.<n>It integrates structured spatial databases with large language models (LLMs) via a hybrid spatial retriever.<n>It formulates the answering process as a multi-objective optimization over spatial and semantic relevance.
• Score: 5.053463027769152
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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