IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v10y2021i11p1266-d683642.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/10/11/1266/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/10/11/1266/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Xiaoxi & Zhang, Yaojun & Yu, Danlin & Qi, Jinghan & Li, Shujing, 2022. "Investigating the spatiotemporal pattern of urban vibrancy and its determinants: Spatial big data analyses in Beijing, China," Land Use Policy, Elsevier, vol. 119(C).
    2. Bo Liu & Desheng Xue & Yiming Tan, 2019. "Deciphering the Manufacturing Production Space in Global City-Regions of Developing Countries—a Case of Pearl River Delta, China," Sustainability, MDPI, vol. 11(23), pages 1-26, December.
    3. Jiaxin Zhang & Zhilin Yu & Yunqin Li & Xueqiang Wang, 2023. "Uncovering Bias in Objective Mapping and Subjective Perception of Urban Building Functionality: A Machine Learning Approach to Urban Spatial Perception," Land, MDPI, vol. 12(7), pages 1-20, June.
    4. Oliveira, Renata Lúcia Magalhães de & Dablanc, Laetitia & Schorung, Matthieu, 2022. "Changes in warehouse spatial patterns and rental prices: Are they related? Exploring the case of US metropolitan areas," Journal of Transport Geography, Elsevier, vol. 104(C).
    5. Eunbee Gil & Yongjin Ahn & Youngsang Kwon, 2020. "Tourist Attraction and Points of Interest (POIs) Using Search Engine Data: Case of Seoul," Sustainability, MDPI, vol. 12(17), pages 1-21, August.
    6. Ting Liu & Gang Cheng & Jie Yang, 2023. "Multi-Scale Recursive Identification of Urban Functional Areas Based on Multi-Source Data," Sustainability, MDPI, vol. 15(18), pages 1-24, September.
    7. Beibei Yu & Zhonghui Wang & Haowei Mu & Li Sun & Fengning Hu, 2019. "Identification of Urban Functional Regions Based on Floating Car Track Data and POI Data," Sustainability, MDPI, vol. 11(23), pages 1-18, November.
    8. Yangyang Wang & Yanjun Liu & Guolei Zhou & Zuopeng Ma & Hongri Sun & Hui Fu, 2022. "Coordinated Relationship between Compactness and Land-Use Efficiency in Shrinking Cities: A Case Study of Northeast China," Land, MDPI, vol. 11(3), pages 1-19, March.
    9. Huang, Xinxin & Wang, Haijun & Xiao, Fentao, 2022. "Simulating urban growth affected by national and regional land use policies: Case study from Wuhan, China," Land Use Policy, Elsevier, vol. 112(C).
    10. Jin, Yushan & Xu, Yuanshuo, 2024. "Carbon reduction of urban form strategies: Regional heterogeneity in Yangtze River Delta, China," Land Use Policy, Elsevier, vol. 141(C).
    11. Su Wu & Neema Simon Sumari & Ting Dong & Gang Xu & Yanfang Liu, 2021. "Characterizing Urban Expansion Combining Concentric-Ring and Grid-Based Analysis for Latin American Cities," Land, MDPI, vol. 10(5), pages 1-14, April.
    12. Anna Busłowska & Jacek Marcinkiewicz, 2023. "Social Cohesion of Functional Urban Areas (Example of Eastern Poland)," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 167(1), pages 451-473, June.
    13. Siyu Ma & Lin Yang & Mei-Po Kwan & Zejun Zuo & Haoyue Qian & Minghao Li, 2021. "Do Individuals’ Activity Structures Influence Their PM 2 . 5 Exposure Levels? Evidence from Human Trajectory Data in Wuhan City," IJERPH, MDPI, vol. 18(9), pages 1-27, April.
    14. Yin, Chenglong & Meng, Fei & Yang, Xinyue & Yang, Fengshuo & Fu, Pingjie & Yao, Guobiao & Chen, Ruishan, 2022. "Spatio-temporal evolution of urban built-up areas and analysis of driving factors —A comparison of typical cities in north and south China," Land Use Policy, Elsevier, vol. 117(C).
    15. Tongwen Wang & Ya Li & Haidong Li & Shuaijun Chen & Hongkai Li & Yunxing Zhang, 2022. "Research on the Vitality Evaluation of Parks and Squares in Medium-Sized Chinese Cities from the Perspective of Urban Functional Areas," IJERPH, MDPI, vol. 19(22), pages 1-23, November.
    16. 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.
    17. Tianle Li & Xinqi Zheng & Chunxiao Zhang & Ruiguo Wang & Jiayu Liu, 2022. "Mining Spatial Correlation Patterns of the Urban Functional Areas in Urban Agglomeration: A Case Study of Four Typical Urban Agglomerations in China," Land, MDPI, vol. 11(6), pages 1-18, June.
    18. Yuting Liang & Yunfeng Hu, 2024. "Regional development assessment based on POIs and Geotree: a case study in Beijing-Tianjin-Hebei region," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(7), pages 18785-18809, July.
    19. Zhenwei Wang & Xiaochun Wang & Zijin Dong & Lisan Li & Wangjun Li & Shicheng Li, 2023. "More Urban Elderly Care Facilities Should Be Placed in Densely Populated Areas for an Aging Wuhan of China," Land, MDPI, vol. 12(1), pages 1-13, January.
    20. Kaixuan Dai & Shi Shen & Changxiu Cheng & Sijing Ye & Peichao Gao, 2020. "Trade-Off Relationship of Arable and Ecological Land in Urban Growth When Altering Urban Form: A Case Study of Shenzhen, China," Sustainability, MDPI, vol. 12(23), pages 1-20, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:10:y:2021:i:11:p:1266-:d:683642. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.