Identifying Urban Functional Areas in China’s Changchun City from Sentinel-2 Images and Social Sensing Data
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/22/4512 |
| _version_ | 1797508600382357504 |
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| author | Shouzhi Chang Zongming Wang Dehua Mao Fusheng Liu Lina Lai Hao Yu |
| author_facet | Shouzhi Chang Zongming Wang Dehua Mao Fusheng Liu Lina Lai Hao Yu |
| author_sort | Shouzhi Chang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T05:06:15Z |
| format | Article |
| id | doaj.art-9b1973c82a9e43acaf0d5bff9f9aa8fc |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T05:06:15Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-9b1973c82a9e43acaf0d5bff9f9aa8fc2023-11-23T01:18:36ZengMDPI AGRemote Sensing2072-42922021-11-011322451210.3390/rs13224512Identifying Urban Functional Areas in China’s Changchun City from Sentinel-2 Images and Social Sensing DataShouzhi Chang0Zongming Wang1Dehua Mao2Fusheng Liu3Lina Lai4Hao Yu5Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaNortheast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaNortheast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaChangchun Municipal Engineering Design and Research Institute, Changchun 130022, ChinaChangchun Municipal Engineering Design and Research Institute, Changchun 130022, ChinaNortheast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaThe 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.https://www.mdpi.com/2072-4292/13/22/4512urban functional areasmobile phone signaling datasocial sensingrandom forestChangchun |
| spellingShingle | Shouzhi Chang Zongming Wang Dehua Mao Fusheng Liu Lina Lai Hao Yu Identifying Urban Functional Areas in China’s Changchun City from Sentinel-2 Images and Social Sensing Data Remote Sensing urban functional areas mobile phone signaling data social sensing random forest Changchun |
| title | Identifying Urban Functional Areas in China’s Changchun City from Sentinel-2 Images and Social Sensing Data |
| title_full | Identifying Urban Functional Areas in China’s Changchun City from Sentinel-2 Images and Social Sensing Data |
| title_fullStr | Identifying Urban Functional Areas in China’s Changchun City from Sentinel-2 Images and Social Sensing Data |
| title_full_unstemmed | Identifying Urban Functional Areas in China’s Changchun City from Sentinel-2 Images and Social Sensing Data |
| title_short | Identifying Urban Functional Areas in China’s Changchun City from Sentinel-2 Images and Social Sensing Data |
| title_sort | identifying urban functional areas in china s changchun city from sentinel 2 images and social sensing data |
| topic | urban functional areas mobile phone signaling data social sensing random forest Changchun |
| url | https://www.mdpi.com/2072-4292/13/22/4512 |
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