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Article: Mapping essential urban land use categories (EULUC) using geospatial big data: Progress, challenges, and opportunities
Title | Mapping essential urban land use categories (EULUC) using geospatial big data: Progress, challenges, and opportunities |
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Authors | |
Keywords | Remote sensing Urban land use type Classification Open big data Machine learning |
Issue Date | 2021 |
Publisher | Taylor & Francis: Open Access Journals. The Journal's web site is located at https://www.tandfonline.com/journals/tbed20 |
Citation | Big Earth Data, 2021, v. 5 n. 3, p. 410-441 How to Cite? |
Abstract | Urban land use information that reflects socio-economic functions and human activities is critically essential for urban planning, landscape design, environmental management, health promotion, and biodiversity conservation. Land-use maps outlining the distribution, pattern, and composition of essential urban land use categories (EULUC) have facilitated a wide spectrum of applications and further triggered new opportunities in urban studies. New and improved Earth observations, algorithms, and advanced products for extracting thematic urban information, in association with emerging social sensing big data and auxiliary crowdsourcing datasets, all together offer great potentials to mapping fine-resolution EULUC from regional to global scales. Here we review the advances of EULUC mapping research and practices in terms of their data, methods, and applications. Based on the historical retrospect, we summarize the challenges and limitations of current EULUC studies regarding sample collection, mixed land use problem, data and model generalization, and large-scale mapping efforts. Finally, we propose and discuss future opportunities, including cross-scale mapping, optimal integration of multi-source features, global sample libraries from crowdsourcing approaches, advanced machine learning and ensembled classification strategy, open portals for data visualization and sharing, multi-temporal mapping of EULUC change, and implications in urban environmental studies, to facilitate multi-scale fine-resolution EULUC mapping research. |
Persistent Identifier | http://hdl.handle.net/10722/304924 |
ISSN | 2023 Impact Factor: 4.2 2023 SCImago Journal Rankings: 0.928 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Chen, B | - |
dc.contributor.author | Xu, B | - |
dc.contributor.author | Gong, P | - |
dc.date.accessioned | 2021-10-05T02:37:11Z | - |
dc.date.available | 2021-10-05T02:37:11Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Big Earth Data, 2021, v. 5 n. 3, p. 410-441 | - |
dc.identifier.issn | 2096-4471 | - |
dc.identifier.uri | http://hdl.handle.net/10722/304924 | - |
dc.description.abstract | Urban land use information that reflects socio-economic functions and human activities is critically essential for urban planning, landscape design, environmental management, health promotion, and biodiversity conservation. Land-use maps outlining the distribution, pattern, and composition of essential urban land use categories (EULUC) have facilitated a wide spectrum of applications and further triggered new opportunities in urban studies. New and improved Earth observations, algorithms, and advanced products for extracting thematic urban information, in association with emerging social sensing big data and auxiliary crowdsourcing datasets, all together offer great potentials to mapping fine-resolution EULUC from regional to global scales. Here we review the advances of EULUC mapping research and practices in terms of their data, methods, and applications. Based on the historical retrospect, we summarize the challenges and limitations of current EULUC studies regarding sample collection, mixed land use problem, data and model generalization, and large-scale mapping efforts. Finally, we propose and discuss future opportunities, including cross-scale mapping, optimal integration of multi-source features, global sample libraries from crowdsourcing approaches, advanced machine learning and ensembled classification strategy, open portals for data visualization and sharing, multi-temporal mapping of EULUC change, and implications in urban environmental studies, to facilitate multi-scale fine-resolution EULUC mapping research. | - |
dc.language | eng | - |
dc.publisher | Taylor & Francis: Open Access Journals. The Journal's web site is located at https://www.tandfonline.com/journals/tbed20 | - |
dc.relation.ispartof | Big Earth Data | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Remote sensing | - |
dc.subject | Urban land use type | - |
dc.subject | Classification | - |
dc.subject | Open big data | - |
dc.subject | Machine learning | - |
dc.title | Mapping essential urban land use categories (EULUC) using geospatial big data: Progress, challenges, and opportunities | - |
dc.type | Article | - |
dc.identifier.email | Chen, B: binleych@hku.hk | - |
dc.identifier.email | Gong, P: penggong@hku.hk | - |
dc.identifier.authority | Chen, B=rp02812 | - |
dc.identifier.authority | Gong, P=rp02780 | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1080/20964471.2021.1939243 | - |
dc.identifier.scopus | eid_2-s2.0-85109642589 | - |
dc.identifier.hkuros | 326089 | - |
dc.identifier.volume | 5 | - |
dc.identifier.issue | 3 | - |
dc.identifier.spage | 410 | - |
dc.identifier.epage | 441 | - |
dc.identifier.isi | WOS:000672742500001 | - |
dc.publisher.place | United Kingdom | - |