{"doi":"10.1145/3589132.3625625","title":"Are Large Language Models Geospatially Knowledgeable?","abstract":null,"journal":"Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems","year":2023,"id":600651,"datarank":0.5983476069846413,"base_score":3.9889840465642745,"endowment":3.9889840465642745,"self_citation_contribution":0.5983476069846413,"citation_network_contribution":0.0,"self_endowment_contribution":0.5983476069846413,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":53,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1539951,"name":"Antonios Anastasopoulos","orcid":"0000-0002-8544-246X","position":1,"is_corresponding":false},{"id":1539952,"name":"Dieter Pfoser","orcid":"0000-0001-9197-0069","position":2,"is_corresponding":false},{"id":1539950,"name":"Prabin Bhandari","orcid":"0009-0006-9034-6372","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Are Large Language Models Geospatially Knowledgeable?","abstract":"Despite the impressive performance of Large Language Models (LLM) for various natural language processing tasks, little is known about their comprehension of geographic data and related ability to facilitate informed geospatial decision-making. This paper investigates the extent of geospatial knowledge, awareness, and reasoning abilities encoded within such pretrained LLMs. With a focus on autoregressive language models, we devise experimental approaches related to (i) probing LLMs for geo-coordinates to assess geospatial knowledge, (ii) using geospatial and non-geospatial prepositions to gauge their geospatial awareness, and (iii) utilizing a multidimensional scaling (MDS) experiment to assess the models' geospatial reasoning capabilities and to determine locations of cities based on prompting. Our results confirm that it does not only take larger but also more sophisticated LLMs to synthesize geospatial knowledge from textual information. As such, this research contributes to understanding the potential and limitations of LLMs in dealing with geospatial information.","is_dataset_classified":null,"base_score":3.970291913552122,"endowment":3.970291913552122,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"21097893","pmcid":null,"openalex_id":"https://openalex.org/W4390100400","authors":[],"funders":[{"funder_name":"National Science Foundation","grant_id":"IIS-212790","title":null},{"funder_name":"National Science Foundation","grant_id":"2018631","title":"MRI: Acquisition of an Adaptive Computing Infrastructure to Support Compute- and Data-Intensive Multidisciplinary Research"},{"funder_name":"National Science Foundation","grant_id":"2127901","title":"III: Small: From Spatial Language to Spatial Data - a simulation-based approach"}],"total_grants":3,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[{"year":2023,"count":1},{"year":2024,"count":16},{"year":2025,"count":32},{"year":2026,"count":3}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://dl.acm.org/doi/pdf/10.1145/3589132.3625625","host_type":""},{"url":"https://dl.acm.org/doi/pdf/10.1145/3589132.3625625","host_type":""},{"url":"https://dl.acm.org/doi/10.1145/3589132.3625625","host_type":"publisher"},{"url":"https://doi.org/10.1145/3589132.3625625","host_type":""},{"url":"https://dx.doi.org/10.48550/arxiv.2310.13002","host_type":""},{"url":"http://arxiv.org/abs/2310.13002","host_type":""},{"url":"https://doi.org/10.48550/arXiv.2310.13002","host_type":""}],"fields_of_study":["Geographic Information Systems Studies","Natural Language Processing Techniques","Topic Modeling","0211 other engineering and technologies","0202 electrical engineering, electronic engineering, information engineering","02 engineering and technology"],"mesh_terms":[],"keywords":["Geospatial analysis","Computer science","Data science","Geographic information system","Comprehension","Geospatial PDF","Geography","Remote sensing","FOS: Computer and information sciences","Computer Science - Computation and Language","Computation and Language (cs.CL)"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Peace, Justice and strong institutions"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-29T13:44:21.514740Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}