Mapping urban functional zones with remote sensing and geospatial big data: a systematic review

作者：Shouhang Du, Shouhang Du, Xiuyuan Zhang, Yichen Lei, Xin Huang, Wei Tu, Bo Liu, Qingyan Meng, Shihong Du, Shihong Du · 发表于：GIScience & Remote Sensing · 年份：2024 · DOI：10.1080/15481603.2024.2404900 · 被引用次数：37 · 研究领域：Impact of Light on Environment and Health、Land Use and Ecosystem Services、Remote-Sensing Image Classification

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, ...