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• W3211670282 abstract "The urban functional area is critical to an understanding of the complex urban system, resource allocation, and management. However, due to urban surveys’ focus on geographic objects and the mixture of urban space, it is difficult to obtain such information. The function of a place is determined by the activities that take place there. This study employed mobile phone signaling data to extract temporal features of human activities through discrete Fourier transform (DFT). Combined with the features extracted from the point of interest (POI) data and Sentinel images, the urban functional areas of Changchun City were identified using a random forest (RF) model. The results indicate that integrating features derived from remote sensing and social sensing data can effectively improve the identification accuracy and that features derived from dynamic mobile phone signaling have a higher identification accuracy than those derived from POI data. The human activity characteristics on weekends are more distinguishable for different functional areas than those on weekdays. The identified urban functional layout of Changchun is consistent with the actual situation. The residential functional area has the highest proportion, accounting for 33.51%, and is mainly distributed in the central area, while the industrial functional area and green-space are distributed around." @default.
• W3211670282 created "2021-11-22" @default.
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• W3211670282 date "2021-11-10" @default.
• W3211670282 modified "2023-09-25" @default.
• W3211670282 title "Identifying Urban Functional Areas in China’s Changchun City from Sentinel-2 Images and Social Sensing Data" @default.
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• W3211670282 doi "https://doi.org/10.3390/rs13224512" @default.
• W3211670282 hasPublicationYear "2021" @default.
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