Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Geospatial Reasoning Questions
This addresses the problem of interpreting natural language geospatial queries for users needing location-based information, representing a novel method for a known bottleneck rather than a foundational advancement.
The paper tackled the problem of answering real-world geospatial questions by bridging the gap between large language models and spatial computing, introducing Spatial-RAG, which significantly improved accuracy, precision, and ranking performance over baselines in experiments on tourism and map-based QA datasets.
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.