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Delineating Urban Functional Zones Using U-Net Deep Learning: Case Study of Kuancheng District, Changchun, China

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  • Yuewen Yang

    (College of Earth Sciences, Jilin University, Changchun 130061, China
    Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130012, China)

  • Dongyan Wang

    (College of Earth Sciences, Jilin University, Changchun 130061, China)

  • Zhuoran Yan

    (College of Earth Sciences, Jilin University, Changchun 130061, China)

  • Shuwen Zhang

    (Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130012, China)

Abstract

Scientific functional zone planning is the key to achieving long-term development goals for cities. The rapid development of remote sensing technology allows for the identification of urban functional zones, which is important since they serve as basic spatial units for urban planning and functioning. The accuracy of three methods—kernel density estimation, term frequency-inverse document frequency, and deep learning—for detecting urban functional zones was investigated using the Gaode points of interest, high-resolution satellite images, and OpenStreetMap. Kuancheng District was divided into twenty-one functional types (five single functional types and twenty mixed ones). The results showed that an approach using deep learning had a higher accuracy than the other two methods for delineating four out of five functions (excluding the commercial function) when compared with a field survey. The field survey showed that Kuancheng District was progressing towards completing the goals of the Land-Use Plan of the Central City of Changchun (2011–2020). Based on these findings, we illustrate the feasibility of identifying urban functional areas and lay out a framework for transforming them. Our results can guide the adjustment of the urban spatial structure and provide a reference basis for the scientific and reasonable development of urban land-use planning.

Suggested Citation

  • Yuewen Yang & Dongyan Wang & Zhuoran Yan & Shuwen Zhang, 2021. "Delineating Urban Functional Zones Using U-Net Deep Learning: Case Study of Kuancheng District, Changchun, China," Land, MDPI, vol. 10(11), pages 1-21, November.
  • Handle: RePEc:gam:jlands:v:10:y:2021:i:11:p:1266-:d:683642
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    References listed on IDEAS

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    1. Ida Bagus Ilham Malik & Bart Julien Dewancker, 2018. "Identification of Population Growth and Distribution, Based on Urban Zone Functions," Sustainability, MDPI, vol. 10(4), pages 1-13, March.
    2. Xiangchun Yu & Zhe Zhang & Lei Wu & Wei Pang & Hechang Chen & Zhezhou Yu & Bin Li, 2020. "Deep Ensemble Learning for Human Action Recognition in Still Images," Complexity, Hindawi, vol. 2020, pages 1-23, January.
    3. Xu, Gang & Zhou, Zhengzi & Jiao, Limin & Zhao, Rui, 2020. "Compact Urban Form and Expansion Pattern Slow Down the Decline in Urban Densities: A Global Perspective," Land Use Policy, Elsevier, vol. 94(C).
    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.
    5. Yunfeng Hu & Yueqi Han, 2019. "Identification of Urban Functional Areas Based on POI Data: A Case Study of the Guangzhou Economic and Technological Development Zone," Sustainability, MDPI, vol. 11(5), pages 1-15, March.
    6. Yandong Wang & Teng Wang & Ming-Hsiang Tsou & Hao Li & Wei Jiang & Fengqin Guo, 2016. "Mapping Dynamic Urban Land Use Patterns with Crowdsourced Geo-Tagged Social Media (Sina-Weibo) and Commercial Points of Interest Collections in Beijing, China," Sustainability, MDPI, vol. 8(11), pages 1-19, November.
    7. Liu, Xin & Jiao, Pengfei & Yuan, Ning & Wang, Wenjun, 2016. "Identification of multi-attribute functional urban areas under a perspective of community detection: A case study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 827-836.
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