@inproceedings{yu-etal-2026-spatial,
    title = "Spatial-{RAG}: Spatial Retrieval Augmented Generation for Real-World Geospatial Reasoning Questions",
    author = "Yu, Dazhou  and
      Bao, Riyang  and
      Ning, Ruiyu  and
      Peng, Jinghong  and
      Mai, Gengchen  and
      Zhao, Liang",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.539/",
    pages = "11094--11112",
    ISBN = "979-8-89176-395-1",
    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 performance over strong baselines."
}