Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/109944
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dc.contributorDepartment of Land Surveying and Geo-Informatics-
dc.creatorWang, S-
dc.creatorHuang, X-
dc.creatorLiu, P-
dc.creatorZhang, M-
dc.creatorBiljecki, F-
dc.creatorHu, T-
dc.creatorFu, X-
dc.creatorLiu, L-
dc.creatorLiu, X-
dc.creatorWang, R-
dc.creatorHuang, Y-
dc.creatorYan, J-
dc.creatorJiang, J-
dc.creatorChukwu, M-
dc.creatorNaghedi, SR-
dc.creatorHemmati, M-
dc.creatorShao, Y-
dc.creatorJia, N-
dc.creatorXiao, Z-
dc.creatorTian, T-
dc.creatorHu, Y-
dc.creatorYu, L-
dc.creatorYap, W-
dc.creatorMacatulad, E-
dc.creatorChen, Z-
dc.creatorCui, Y-
dc.creatorIto, K-
dc.creatorYe, M-
dc.creatorFan, Z-
dc.creatorLei, B-
dc.creatorBao, S-
dc.date.accessioned2024-11-20T07:30:29Z-
dc.date.available2024-11-20T07:30:29Z-
dc.identifier.issn1569-8432-
dc.identifier.urihttp://hdl.handle.net/10397/109944-
dc.language.isoenen_US
dc.publisherElsevier BVen_US
dc.rights© 2024 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).en_US
dc.rightsThe following publication Wang, S., Huang, X., Liu, P., Zhang, M., Biljecki, F., Hu, T., Fu, X., Liu, L., Liu, X., Wang, R., Huang, Y., Yan, J., Jiang, J., Chukwu, M., Reza Naghedi, S., Hemmati, M., Shao, Y., Jia, N., Xiao, Z., . . . Bao, S. (2024). Mapping the landscape and roadmap of geospatial artificial intelligence (GeoAI) in quantitative human geography: An extensive systematic review. International Journal of Applied Earth Observation and Geoinformation, 128, 103734 is available at https://doi.org/10.1016/j.jag.2024.103734.en_US
dc.subjectGeoAIen_US
dc.subjectGeographic subdomainsen_US
dc.subjectGeospatial artificial intelligenceen_US
dc.subjectHuman geographyen_US
dc.subjectSystematic reviewen_US
dc.titleMapping the landscape and roadmap of geospatial artificial intelligence (GeoAI) in quantitative human geography : an extensive systematic reviewen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume128-
dc.identifier.doi10.1016/j.jag.2024.103734-
dcterms.abstractThis paper brings a comprehensive systematic review of the application of geospatial artificial intelligence (GeoAI) in quantitative human geography studies, including the subdomains of cultural, economic, political, historical, urban, population, social, health, rural, regional, tourism, behavioural, environmental and transport geography. In this extensive review, we obtain 14,537 papers from the Web of Science in the relevant fields and select 1516 papers that we identify as human geography studies using GeoAI via human scanning conducted by several research groups around the world. We outline the GeoAI applications in human geography by systematically summarising the number of publications over the years, empirical studies across countries, the categories of data sources used in GeoAI applications, and their modelling tasks across different subdomains. We find out that existing human geography studies have limited capacity to monitor complex human behaviour and examine the non-linear relationship between human behaviour and its potential drivers—such limits can be overcome by GeoAI models with the capacity to handle complexity. We elaborate on the current progress and status of GeoAI applications within each subdomain of human geography, point out the issues and challenges, as well as propose the directions and research opportunities for using GeoAI in future human geography studies in the context of sustainable and open science, generative AI, and quantum revolution.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationInternational journal of applied earth observation and geoinformation, Apr. 2024, v. 128, 103734-
dcterms.isPartOfInternational journal of applied earth observation and geoinformation-
dcterms.issued2024-04-
dc.identifier.scopus2-s2.0-85187354023-
dc.identifier.eissn1872-826X-
dc.identifier.artn103734-
dc.description.validate202411 bcch-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextUS National Science Foundation Awards; Japan Society for the Promotion of Science KAKENHI research grant; Project Large-scale 3D Geospatial Data for Urban Analytics, which is supported by the National University of Singapore (NUS); Project Multi-scale Digital Twins for the Urban Environment: From Heartbeats to Cities, which is supported by the Singapore Ministry of Education Academic Research Fund Tier 1; Singapore International Graduate Award (SINGA) scholarship provided by the Agency for Science, Technology, and Research (A*STAR) and the NUS; NUS President’s Graduate Fellowship; Foreign PhD Scholarship Grant of the Department of Science and Technology - Engineering Research and Development for Technology, Philippines; NUS Graduate Research Scholarshipen_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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