Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Geospatial Reasoning Questions
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866910999673569280 |
|---|---|
| author | Yu, Dazhou Bao, Riyang Ning, Ruiyu Peng, Jinghong Mai, Gengchen Zhao, Liang |
| author_facet | Yu, Dazhou Bao, Riyang Ning, Ruiyu Peng, Jinghong Mai, Gengchen Zhao, Liang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_18470 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Geospatial Reasoning Questions Yu, Dazhou Bao, Riyang Ning, Ruiyu Peng, Jinghong Mai, Gengchen Zhao, Liang Information Retrieval Emerging Technologies Machine Learning 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. |
| title | Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Geospatial Reasoning Questions |
| topic | Information Retrieval Emerging Technologies Machine Learning |
| url | https://arxiv.org/abs/2502.18470 |