Mapping urban functional zones with remote sensing and geospatial big data: a systematic review
| Autor: | Shouhang Du, Xiuyuan Zhang, Yichen Lei, Xin Huang, Wei Tu, Bo Liu, Qingyan Meng, Shihong Du |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | GIScience & Remote Sensing, Vol 61, Iss 1 (2024) |
| Druh dokumentu: | article |
| ISSN: | 15481603 1943-7226 1548-1603 |
| DOI: | 10.1080/15481603.2024.2404900 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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