mlsanityData to 14 Oct 2026

Much of Geospatial Web Search Is Beyond Traditional GIS

arXiv 2605.11336 ↗First posted 11 May 2026cs.IR cs.AI cs.CLcited by 0

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Abstract (arXiv)

Web search queries concern place far more often than existing labelling schemes suggest, yet the landscape of geospatial web search queries - what people ask of place, and how often - remains poorly characterised at scale. We apply dense sentence embeddings, a lightweight SetFit classifier, and density-based clustering to the full MS MARCO corpus of 1.01 million real Bing queries without prior filtering for toponyms or spatial keywords, identifying 181,827 geospatial queries (18.0%), nearly threefold the 6.17% labelled as Location in the original annotations. The resulting taxonomy of 88 query categories reveals that geospatial web search is dominated by transactional and practical lookups: costs and prices alone account for 15.3% of geospatial queries, nearly twice the size of the entire physical geography theme. Much of this activity - costs, opening hours, contact details, weather, travel recommendations - falls outside the scope of what traditional GIS and knowledge graphs are built to serve. The categories vary substantially in the kind of answer they admit, from deterministic lookups answerable from spatial databases or knowledge graphs to evaluative or temporally volatile queries that require generative or real-time systems. We discuss implications for hybrid retrieval architectures and for benchmarks of geographic reasoning in large language models. We openly release the labelled dataset, classifier, and taxonomy.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 50 references that are in the map. First posted on arXiv 11 May 2026.

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16 of 50 references.

References · 16

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  2. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,898
  3. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,605
  4. 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 801
  5. 2022MTEB: Massive Text Embedding Benchmarkcited by 640
  6. 2022Efficient Few-Shot Learning Without Promptscited by 137
  7. 2020Top2Vec: Distributed Representations of Topicscited by 126
  8. 2020Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too!cited by 73
  9. 2023GPT4GEO: How a Language Model Sees the World's Geographycited by 68
  10. 2023GeoLLM: Extracting Geospatial Knowledge from Large Language Modelscited by 64
  11. 2023Are Large Language Models Geospatially Knowledgeable?cited by 30
  12. 2021Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directionscited by 24
4 more
  1. 2020Template-Based Question Answering over Linked Geospatial Datacited by 12
  2. 2024Quantifying Geospatial in the Common Crawl Corpuscited by 4
  3. 2024Do Sentence Transformers Learn Quasi-Geospatial Concepts from General Text?cited by 3
  4. 2026The Scenic Route to Deception: Dark Patterns and Explainability Pitfalls in Conversational Navigationcited by 1

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  5. 2025MapQA: Open-domain Geospatial Question Answering on Map Data
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