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

• URL: http://arxiv.org/abs/2502.18470v3
• Date: Fri, 14 Mar 2025 02:48:55 GMT
• Title: Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Spatial Reasoning Questions
• Authors: Dazhou Yu, Riyang Bao, Gengchen Mai, Liang Zhao,
• Abstract summary: We propose Spatial Retrieval-Augmented Generation (Spatial-RAG), a framework that extends RAG to spatial tasks.<n>A multi-objective ranking strategy balances spatial constraints and semantic relevance, while an LLM-guided generator ensures coherent responses.
• Score: 5.744799747144805
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Spatial reasoning remains a challenge for Large Language Models (LLMs), which struggle with spatial data retrieval and reasoning. We propose Spatial Retrieval-Augmented Generation (Spatial-RAG), a framework that extends RAG to spatial tasks by integrating sparse spatial retrieval (spatial databases) and dense semantic retrieval (LLM-based similarity). A multi-objective ranking strategy balances spatial constraints and semantic relevance, while an LLM-guided generator ensures coherent responses. Experiments on a real-world tourism dataset show that Spatial-RAG significantly improves spatial question answering, bridging the gap between LLMs and spatial intelligence.

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