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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.

Land covers provide essential information for understanding and detecting ecosystem, resources, and environmental dynamics. However, they are generally mapped at coarser temporal scales to study the inter-annual changes, while scant attention has been paid to map intra-annual land cover dynamics at finer temporal scales. Moreover, existing studies are still limited in intra-annual land cover mapping with dense satellite image time series (SITS). Accordingly, this study proposed a novel approach to accurately classify dense SITS for mapping intra-annual land cover dynamics. First, dense SITS is segmented at multiple spatiotemporal scales to generate optimal spatiotemporal cubes (ST-cubes), which are chosen as classification units. Second, the ST-cubes based on spectral, textural, spatial, and temporal features are integratively defined and employed in SITS classification. Third, the spatiotemporal context is modeled by a spatiotemporally extended conditional random field model that measures both spatiotemporal features and semantic correlation between geographic objects. Finally, the proposed method is applied to map the intra-annual land cover dynamics. Comparative experiments of SITS classification are carried out between our method and three existing competitors in a suburban area in Beijing, China, with a dense Sentinel-2 SITS. Moreover, based on the classification results, we analyzed the quantitative intra-annual dynamics of land cover. The result shows that our approach achieves significant improvements in classification accuracy over existing methods, indicating the effectiveness and superiority of the proposed method in mapping intra-annual land cover dynamics with dense SITS.