Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/112957
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dc.contributorDepartment of Land Surveying and Geo-Informatics-
dc.contributorResearch Centre for Artificial Intelligence in Geomatics-
dc.creatorWeng, Qen_US
dc.creatorLi, Zen_US
dc.creatorCao, Yen_US
dc.creatorLu, Xen_US
dc.creatorGamba, Pen_US
dc.creatorZhu, Xen_US
dc.creatorXu, Yen_US
dc.creatorZhang, Fen_US
dc.creatorQin, Ren_US
dc.creatorYang, MYen_US
dc.creatorMa, Pen_US
dc.creatorHuang, Wen_US
dc.creatorYin, Ten_US
dc.creatorZheng, Qen_US
dc.creatorZhou, Yen_US
dc.creatorAsner, Gen_US
dc.date.accessioned2025-05-15T07:00:17Z-
dc.date.available2025-05-15T07:00:17Z-
dc.identifier.urihttp://hdl.handle.net/10397/112957-
dc.language.isoenen_US
dc.publisherNature Publishing Groupen_US
dc.rightsOpen Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.en_US
dc.rights© The Author(s) 2024en_US
dc.rightsThe following publication Weng, Q., Li, Z., Cao, Y. et al. How will ai transform urban observing, sensing, imaging, and mapping?. npj Urban Sustain 4, 50 (2024) is available at https://doi.org/10.1038/s42949-024-00188-3.en_US
dc.titleHow will ai transform urban observing, sensing, imaging, and mapping?en_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume4en_US
dc.identifier.doi10.1038/s42949-024-00188-3en_US
dcterms.abstractAdvances in artificial intelligence (AI) and Earth observation (EO) have transformed urban studies. This paper provides a commentary on how the AI-EO integration offers advancements in urban studies and applications. We conclude that AI will provide a deeper interpretation and autonomous identification of urban issues and the creation of customized urban designs. Open issues remain, especially in integrating diverse geospatial big data, data security, and developing a general analytical framework.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationnpj Urban Sustainability, 2024, v. 4, 50en_US
dcterms.isPartOfnpj Urban Sustainabilityen_US
dcterms.issued2024-
dc.identifier.scopus2-s2.0-85210559330-
dc.identifier.eissn2661-8001en_US
dc.identifier.artn50en_US
dc.description.validate202505 bcch-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOS, a3789-
dc.identifier.SubFormID51091-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextGlobal STEM Professorship, Hong Kong SAR Government (P0039329); Hong Kong Polytechnic University (P0046482 and P0038446)en_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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