Mapping urban functional zones with remote sensing and geospatial big data: a systematic review
Urban functional zones (UFZs) serve as the spatial carriers embodying urban economic and social activities, thus making the accurate mapping of UFZs imperative for urban planning, management, and sustainable development. Traditional remote sensing-based methods for mapping UFZs primarily capture the...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | GIScience & Remote Sensing |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/15481603.2024.2404900 |
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| author | Shouhang Du Xiuyuan Zhang Yichen Lei Xin Huang Wei Tu Bo Liu Qingyan Meng Shihong Du |
| author_facet | Shouhang Du Xiuyuan Zhang Yichen Lei Xin Huang Wei Tu Bo Liu Qingyan Meng Shihong Du |
| author_sort | Shouhang Du |
| collection | DOAJ |
| description | Urban functional zones (UFZs) serve as the spatial carriers embodying urban economic and social activities, thus making the accurate mapping of UFZs imperative for urban planning, management, and sustainable development. Traditional remote sensing-based methods for mapping UFZs primarily capture the physical attributes of ground objects (such as land cover and spatial patterns) while overlooking the inherent social and economic characteristics, as well as the comprehensiveness, heterogeneity, and scale-dependency. With the rapid development of intelligent sensors, the available geospatial big data, reflecting individual human activities, have greatly increased and enable users to analyze UFZs from both physical and socioeconomic aspects. In this study, we provide a comprehensive review of the existing literature on UFZ mapping using remote sensing and geospatial big data. Specifically, this study summarizes the state of the art from three perspectives: spatial analysis units, representation features derived from multi-source data, and the function classification methods of UFZs. Spatial analysis units encompass regular grids, road blocks, image segmentation units, traffic analysis zones, and buildings. Data features consist of the remote sensing image-derived features (such as visual, spatial pattern, and abstract features) and the geospatial big data-derived features (such as spatial, attribute, and temporal features). For function classification, kernel density estimation, cluster analysis, supervised machine learning, probabilistic topic models, and deep learning methods have been applied. Finally, this study discusses the challenges and limitations of UFZ mapping units, the bias issues of geospatial big data, and the integration of remote sensing and geospatial big data. Meanwhile, future opportunities to these issues and the expansion of functions from 2D to 3D are discussed, in order to formulate an enhanced UFZ mapping framework. |
| format | Article |
| id | doaj-art-fe382fa5d1454996844260cb377ccc7f |
| institution | Kabale University |
| issn | 1548-1603 1943-7226 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | GIScience & Remote Sensing |
| spelling | doaj-art-fe382fa5d1454996844260cb377ccc7f2024-12-06T13:51:51ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262024-12-0161110.1080/15481603.2024.2404900Mapping urban functional zones with remote sensing and geospatial big data: a systematic reviewShouhang Du0Xiuyuan Zhang1Yichen Lei2Xin Huang3Wei Tu4Bo Liu5Qingyan Meng6Shihong Du7College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing, ChinaCollege of Urban and Environmental Sciences, Peking University, Beijing, ChinaCollege of Urban and Environmental Sciences, Peking University, Beijing, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, ChinaSchool of Architecture & Urban Planning, Shenzhen University, Shenzhen, ChinaThe Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaThe Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaCollege of Urban and Environmental Sciences, Peking University, Beijing, ChinaUrban functional zones (UFZs) serve as the spatial carriers embodying urban economic and social activities, thus making the accurate mapping of UFZs imperative for urban planning, management, and sustainable development. Traditional remote sensing-based methods for mapping UFZs primarily capture the physical attributes of ground objects (such as land cover and spatial patterns) while overlooking the inherent social and economic characteristics, as well as the comprehensiveness, heterogeneity, and scale-dependency. With the rapid development of intelligent sensors, the available geospatial big data, reflecting individual human activities, have greatly increased and enable users to analyze UFZs from both physical and socioeconomic aspects. In this study, we provide a comprehensive review of the existing literature on UFZ mapping using remote sensing and geospatial big data. Specifically, this study summarizes the state of the art from three perspectives: spatial analysis units, representation features derived from multi-source data, and the function classification methods of UFZs. Spatial analysis units encompass regular grids, road blocks, image segmentation units, traffic analysis zones, and buildings. Data features consist of the remote sensing image-derived features (such as visual, spatial pattern, and abstract features) and the geospatial big data-derived features (such as spatial, attribute, and temporal features). For function classification, kernel density estimation, cluster analysis, supervised machine learning, probabilistic topic models, and deep learning methods have been applied. Finally, this study discusses the challenges and limitations of UFZ mapping units, the bias issues of geospatial big data, and the integration of remote sensing and geospatial big data. Meanwhile, future opportunities to these issues and the expansion of functions from 2D to 3D are discussed, in order to formulate an enhanced UFZ mapping framework.https://www.tandfonline.com/doi/10.1080/15481603.2024.2404900Urban functional zonesremote sensinggeospatial big datamultimodal fusionreview |
| spellingShingle | Shouhang Du Xiuyuan Zhang Yichen Lei Xin Huang Wei Tu Bo Liu Qingyan Meng Shihong Du Mapping urban functional zones with remote sensing and geospatial big data: a systematic review GIScience & Remote Sensing Urban functional zones remote sensing geospatial big data multimodal fusion review |
| title | Mapping urban functional zones with remote sensing and geospatial big data: a systematic review |
| title_full | Mapping urban functional zones with remote sensing and geospatial big data: a systematic review |
| title_fullStr | Mapping urban functional zones with remote sensing and geospatial big data: a systematic review |
| title_full_unstemmed | Mapping urban functional zones with remote sensing and geospatial big data: a systematic review |
| title_short | Mapping urban functional zones with remote sensing and geospatial big data: a systematic review |
| title_sort | mapping urban functional zones with remote sensing and geospatial big data a systematic review |
| topic | Urban functional zones remote sensing geospatial big data multimodal fusion review |
| url | https://www.tandfonline.com/doi/10.1080/15481603.2024.2404900 |
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