Urban Functional Zone Identification Based on Multimodal Data Fusion: A Case Study of Chongqing’s Central Urban Area
Urban Functional Zones (UFZs) are spatial units of the city divided according to specific functional activities. Detailed identification of UFZs is vital for optimizing urban management, guiding planning and design, and promoting sustainable development. However, existing UFZ recognition methods fac...
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MDPI AG
2025-03-01
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| author | Yongchuan Zhang Yuhong Xu Jie Gao Zunya Zhao Jing Sun Fengyun Mu |
| author_facet | Yongchuan Zhang Yuhong Xu Jie Gao Zunya Zhao Jing Sun Fengyun Mu |
| author_sort | Yongchuan Zhang |
| collection | DOAJ |
| description | Urban Functional Zones (UFZs) are spatial units of the city divided according to specific functional activities. Detailed identification of UFZs is vital for optimizing urban management, guiding planning and design, and promoting sustainable development. However, existing UFZ recognition methods face significant challenges, such as difficulties in effectively integrating multi-source heterogeneous data, capturing dynamic spatiotemporal patterns, and addressing the complex interrelationships among various data types. These issues significantly limit the applicability of UFZ mapping in complex urban scenarios. To address these challenges, this paper proposes a tripartite neural network (TriNet) for multimodal data processing, including Remote Sensing (RS) images, Point of Interest (POI) data, and Origin–Destination (OD) data, fully utilizing the complementarity of different data types. TriNet comprises three specialized branches: ImgNet for spatial features extraction from images, POINet for functional density distribution features extraction from POI data, and TrajNet for spatiotemporal pattern features extraction from OD data. Finally, the method deeply fuses these features through a feature fusion module, which utilizes a two-layer fully connected network for deep fusion, allowing the model to fully utilize the interdependencies among the data types, significantly improving the UFZ classification accuracy. The experimental data are generated by mapping OpenStreetMap (OSM) vector into conceptual representations, integrating images with social sensing data to create a comprehensive UFZ classification benchmark. The method achieved an overall accuracy of 84.13% on the test set of Chongqing’s main urban area, demonstrating high accuracy and robustness in UFZ classification tasks. The experimental results show that the TriNet model performs effectively in UFZ classification. |
| format | Article |
| id | doaj-art-e7df1035d9574eb9bbbca0c5b46f9fb5 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-e7df1035d9574eb9bbbca0c5b46f9fb52025-08-20T01:48:54ZengMDPI AGRemote Sensing2072-42922025-03-0117699010.3390/rs17060990Urban Functional Zone Identification Based on Multimodal Data Fusion: A Case Study of Chongqing’s Central Urban AreaYongchuan Zhang0Yuhong Xu1Jie Gao2Zunya Zhao3Jing Sun4Fengyun Mu5Smart City Department, Chongqing Jiaotong University, Chongqing 402260, ChinaSmart City Department, Chongqing Jiaotong University, Chongqing 402260, ChinaSmart City Department, Chongqing Jiaotong University, Chongqing 402260, ChinaSmart City Department, Chongqing Jiaotong University, Chongqing 402260, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaSmart City Department, Chongqing Jiaotong University, Chongqing 402260, ChinaUrban Functional Zones (UFZs) are spatial units of the city divided according to specific functional activities. Detailed identification of UFZs is vital for optimizing urban management, guiding planning and design, and promoting sustainable development. However, existing UFZ recognition methods face significant challenges, such as difficulties in effectively integrating multi-source heterogeneous data, capturing dynamic spatiotemporal patterns, and addressing the complex interrelationships among various data types. These issues significantly limit the applicability of UFZ mapping in complex urban scenarios. To address these challenges, this paper proposes a tripartite neural network (TriNet) for multimodal data processing, including Remote Sensing (RS) images, Point of Interest (POI) data, and Origin–Destination (OD) data, fully utilizing the complementarity of different data types. TriNet comprises three specialized branches: ImgNet for spatial features extraction from images, POINet for functional density distribution features extraction from POI data, and TrajNet for spatiotemporal pattern features extraction from OD data. Finally, the method deeply fuses these features through a feature fusion module, which utilizes a two-layer fully connected network for deep fusion, allowing the model to fully utilize the interdependencies among the data types, significantly improving the UFZ classification accuracy. The experimental data are generated by mapping OpenStreetMap (OSM) vector into conceptual representations, integrating images with social sensing data to create a comprehensive UFZ classification benchmark. The method achieved an overall accuracy of 84.13% on the test set of Chongqing’s main urban area, demonstrating high accuracy and robustness in UFZ classification tasks. The experimental results show that the TriNet model performs effectively in UFZ classification.https://www.mdpi.com/2072-4292/17/6/990multi-source dataremote sensingsocial sensingdeep learningurban functional zone mapping |
| spellingShingle | Yongchuan Zhang Yuhong Xu Jie Gao Zunya Zhao Jing Sun Fengyun Mu Urban Functional Zone Identification Based on Multimodal Data Fusion: A Case Study of Chongqing’s Central Urban Area Remote Sensing multi-source data remote sensing social sensing deep learning urban functional zone mapping |
| title | Urban Functional Zone Identification Based on Multimodal Data Fusion: A Case Study of Chongqing’s Central Urban Area |
| title_full | Urban Functional Zone Identification Based on Multimodal Data Fusion: A Case Study of Chongqing’s Central Urban Area |
| title_fullStr | Urban Functional Zone Identification Based on Multimodal Data Fusion: A Case Study of Chongqing’s Central Urban Area |
| title_full_unstemmed | Urban Functional Zone Identification Based on Multimodal Data Fusion: A Case Study of Chongqing’s Central Urban Area |
| title_short | Urban Functional Zone Identification Based on Multimodal Data Fusion: A Case Study of Chongqing’s Central Urban Area |
| title_sort | urban functional zone identification based on multimodal data fusion a case study of chongqing s central urban area |
| topic | multi-source data remote sensing social sensing deep learning urban functional zone mapping |
| url | https://www.mdpi.com/2072-4292/17/6/990 |
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