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A New Approach to Monitoring Urban Built-Up Areas in Kunming and Yuxi from 2012 to 2021: Promoting Healthy Urban Development and Efficient Governance

Author

Listed:
  • Jun Zhang

    (School of Architecture and Planning, Yunnan University, Kunming 650031, China)

  • Xue Zhang

    (School of Architecture and Planning, Yunnan University, Kunming 650031, China)

  • Xueping Tan

    (School of Architecture and Planning, Yunnan University, Kunming 650031, China)

  • Xiaodie Yuan

    (School of Architecture and Planning, Yunnan University, Kunming 650031, China
    School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China)

Abstract

With the rapid expansion of urban built-up areas in recent years, accurate and long time series monitoring of urban built-up areas is of great significance for healthy urban development and efficient governance. As the basic carrier of urban activities, the accurate monitoring of urban built-up areas can also assist in the formulation of urban planning. Previous studies on urban built-up areas mainly focus on the analysis of a single time section, which makes the extraction results exist with a certain degree of contingency. In this study, a U-net is used to extract and monitor urban built-up areas in the Kunming and Yuxi area from 2012 to 2021 based on nighttime light data and POI_NTL (Point of Interest_Nighttime light) data. The results show that the highest accuracy of single nighttime light (NTL) data extraction was 89.31%, and that of POI_NTL data extraction was 95.31%, which indicates that data fusion effectively improves the accuracy of built-up area extraction. Additionally, the comparative analysis of the results of built-up areas and the actual development of the city shows that NTL data is more susceptible to urban emergencies in the extraction of urban built-up areas, and POI (Point of interest) data is subject to the level of technology and service available in the region, while the combination of the two can avoid the occasional impact of single data as much as possible. This study deeply analyzes the results of extracting urban built-up areas from different data in different periods and obtains the feasible method for the long time sequence monitoring of urban built-up areas, which has important theoretical and practical significance for the formulation of long-term urban planning and the current high-quality urban development.

Suggested Citation

  • Jun Zhang & Xue Zhang & Xueping Tan & Xiaodie Yuan, 2022. "A New Approach to Monitoring Urban Built-Up Areas in Kunming and Yuxi from 2012 to 2021: Promoting Healthy Urban Development and Efficient Governance," IJERPH, MDPI, vol. 19(19), pages 1-18, September.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:19:p:12198-:d:925703
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    References listed on IDEAS

    as
    1. Shi, Kaifang & Yu, Bailang & Huang, Chang & Wu, Jianping & Sun, Xiufeng, 2018. "Exploring spatiotemporal patterns of electric power consumption in countries along the Belt and Road," Energy, Elsevier, vol. 150(C), pages 847-859.
    2. Jun Zhang & Xiaodie Yuan & Xueping Tan & Xue Zhang, 2021. "Delineation of the Urban-Rural Boundary through Data Fusion: Applications to Improve Urban and Rural Environments and Promote Intensive and Healthy Urban Development," IJERPH, MDPI, vol. 18(13), pages 1-19, July.
    3. Jun Zhang & Xue Zhang & Xueping Tan & Xiaodie Yuan, 2022. "Extraction of Urban Built-Up Area Based on Deep Learning and Multi-Sources Data Fusion—The Application of an Emerging Technology in Urban Planning," Land, MDPI, vol. 11(8), pages 1-19, August.
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    2. Xiaodie Yuan & Baoyu Chen & Xiong He & Guojun Zhang & Chunshan Zhou, 2024. "Spatial Differentiation and Influencing Factors of Tertiary Industry in the Pearl River Delta Urban Agglomeration," Land, MDPI, vol. 13(2), pages 1-23, February.

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