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Urban Functional Zone Classification via Advanced Multi-Modal Data Fusion

Author

Listed:
  • Tianyu Liu

    (College of Information and Technology, Jilin Agricultural University, Changchun 130118, China)

  • Hongbing Chen

    (College of Information and Technology, Jilin Agricultural University, Changchun 130118, China
    Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518034, China)

  • Junfeng Ren

    (College of Information and Technology, Jilin Agricultural University, Changchun 130118, China)

  • Long Zhang

    (College of Information and Technology, Jilin Agricultural University, Changchun 130118, China)

  • Hongrui Chen

    (Computer Technology and Engineering, Changchun Institute of Technology, Changchun 130000, China)

  • Rundong Hong

    (College of Information and Technology, Jilin Agricultural University, Changchun 130118, China)

  • Chenshuang Li

    (College of Information and Technology, Jilin Agricultural University, Changchun 130118, China)

  • Wenlong Cui

    (College of Information and Technology, Jilin Agricultural University, Changchun 130118, China)

  • Wenhua Guo

    (Information Center of Ministry of Natural Resources, Beijing 100830, China
    Technology Innovation Center for Territorial & Spatial Big Data, Ministry of Natural Resources, Beijing 100830, China)

  • Changji Wen

    (College of Information and Technology, Jilin Agricultural University, Changchun 130118, China
    Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518034, China)

Abstract

The classification of urban functional zones is crucial for improving land use efficiency and promoting balanced development across urban areas. Existing methods for classifying urban functional zones using mobile signaling data face challenges primarily due to the limitations of single data sources, insufficient utilization of multidimensional data, and inherent inaccuracies in mobile signaling data. To address these issues, this study proposes an innovative classification method that employs advanced multimodal data fusion techniques to enhance the accuracy and reliability of functional zone classification. Mobile signaling data are mapped into image data using timestamp and geographic location information and combined with point of interest (POI) data to construct a comprehensive multimodal dataset. Deep learning techniques are then applied to fuse the multimodal data features, enabling precise and reliable classification of functional zones. The experimental results demonstrate that this method achieves an accuracy of 95.128% in classifying urban functional zones, significantly outperforming methods that use single-modal data.

Suggested Citation

  • Tianyu Liu & Hongbing Chen & Junfeng Ren & Long Zhang & Hongrui Chen & Rundong Hong & Chenshuang Li & Wenlong Cui & Wenhua Guo & Changji Wen, 2024. "Urban Functional Zone Classification via Advanced Multi-Modal Data Fusion," Sustainability, MDPI, vol. 16(24), pages 1-26, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:24:p:11145-:d:1547547
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    References listed on IDEAS

    as
    1. Sunil Kumar & Sushil Kumar Singh & Sudeep Varshney & Saurabh Singh & Prashant Kumar & Bong-Gyu Kim & In-Ho Ra, 2023. "Fusion of Deep Sort and Yolov5 for Effective Vehicle Detection and Tracking Scheme in Real-Time Traffic Management Sustainable System," Sustainability, MDPI, vol. 15(24), pages 1-24, December.
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    4. Ruomu Miao & Yuxia Wang & Shuang Li, 2021. "Analyzing Urban Spatial Patterns and Functional Zones Using Sina Weibo POI Data: A Case Study of Beijing," Sustainability, MDPI, vol. 13(2), pages 1-15, January.
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