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The benchmark systematically spans intrinsic and extrinsic, static and dynamic, and geographic and non\u2010geographic dimensions, comprising 851 image\u2010based tasks. These tasks cover a variety of tasks including spatial visualization, spatial relation reasoning, scene interpretation, spatial orientation and localization, and map\u2010based problem\u2010solving. Seven state\u2010of\u2010the\u2010art MLLMs were tested under zero\u2010shot, zero\u2010CoT, and one\u2010shot prompting strategies. Results indicate that overall accuracies remain moderate to low, with significant variability across models, languages, and task types. Prompting strategies yield only limited improvements, underscoring that engineering alone cannot compensate for fundamental deficits in spatial cognition. Moreover, a scale\u2010separation effect was observed, with distinct performance patterns between geographic and non\u2010geographic tasks, as well as between small\u2010 and large\u2010scale contexts. These findings reveal the incomplete integration of visual, linguistic, and spatial reasoning in current MLLMs. GVSABench offers a reproducible and cognitively grounded framework for advancing future research on robust and human\u2010aligned spatial intelligence.<\/jats:p>","DOI":"10.1111\/tgis.70189","type":"journal-article","created":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T03:59:06Z","timestamp":1770782346000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Can AI Observe Geographical Space? GVSABench For Evaluating Geo\u2010Visuospatial Ability of Large Multimodal Models"],"prefix":"10.1111","volume":"30","author":[{"given":"Can","family":"Liu","sequence":"first","affiliation":[{"name":"Hunan Key Laboratory of Geospatial Big Data Mining and Application, School of Geographical Sciences Hunan Normal University  Changsha China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiwei","family":"Wei","sequence":"additional","affiliation":[{"name":"Hunan Key Laboratory of Geospatial Big Data Mining and Application, School of Geographical Sciences Hunan Normal University  Changsha China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hua","family":"Liao","sequence":"additional","affiliation":[{"name":"Hunan Key Laboratory of Geospatial Big Data Mining and Application, School of Geographical Sciences Hunan Normal University  Changsha China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weihua","family":"Dong","sequence":"additional","affiliation":[{"name":"Advanced Interdisciplinary Institute of Satellite Applications, State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science Beijing Normal University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,2,10]]},"reference":[{"key":"e_1_2_10_2_1","unstructured":"Anthropic S.2024.\u201cModel Card Addendum: Claude 3.5 Haiku and Upgraded Claude 3.5 Sonnet.\u201dhttps:\/\/www.semanticscholar.org\/paper\/Model\u2010Card\u2010Addendum:\u2010Claude\u20103.5\u2010Haiku\u2010and\u2010Upgraded\u2010Anthropic\/c7822cdc35ad788ec87e14b3a9d45010f1f86c38."},{"key":"e_1_2_10_3_1","doi-asserted-by":"crossref","unstructured":"Balloccu S. 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