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Enhancing Urban Land Use Identification Using Urban Morphology

Author

Listed:
  • Chuan Lin

    (Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, School of Public Administration, South China Agricultural University, Guangzhou 510640, China
    These authors contributed equally to this work.)

  • Guang Li

    (Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, School of Public Administration, South China Agricultural University, Guangzhou 510640, China
    These authors contributed equally to this work.)

  • Zegen Zhou

    (Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, School of Public Administration, South China Agricultural University, Guangzhou 510640, China)

  • Jia Li

    (Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, School of Public Administration, South China Agricultural University, Guangzhou 510640, China)

  • Hongmei Wang

    (Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, School of Public Administration, South China Agricultural University, Guangzhou 510640, China)

  • Yilun Liu

    (Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, School of Public Administration, South China Agricultural University, Guangzhou 510640, China)

Abstract

Urban land use provides essential information about how land is utilized within cities, which is critical for land planning, urban renewal, and early warnings for natural disasters. Although existing studies have utilized multi-source perception data to acquire land use information quickly and at low cost, and some have integrated urban morphological indicators to aid in land use identification, there is still a lack of systematic discussion in the literature regarding the potential of three-dimensional urban morphology to enhance identification effectiveness. Therefore, this paper aims to explore how urban three-dimensional morphology can be used to improve the identification of urban land use types. This study presents an innovative approach called the UMH–LUC model to enhance the accuracy of urban land use identification. The model first conducts a preliminary classification using points of interest (POI) data. It then improves the results with a dynamic reclassification based on floor area ratio (FAR) measurements and a variance reclassification using area and perimeter metrics. These methodologies leverage key urban morphological features to distinguish land use types more precisely. The model was validated in the Pearl River Delta urban agglomeration using random sampling, comparative analysis and case studies. Results demonstrate that the UMH–LUC model achieved an identification accuracy of 81.7% and a Kappa coefficient of 77.6%, representing an 11.9% improvement over a non-morphology-based approach. Moreover, the overall disagreement for UMH–LUC is 0.183, a reduction of 0.099 compared to LUC without urban morphology and 0.19 compared to EULUC-China. The model performed particularly well in identifying residential land, mixed-use areas and marginal lands. This confirms urban morphology’s value in supporting low-cost, efficient land use mapping with applications for sustainable planning and management.

Suggested Citation

  • Chuan Lin & Guang Li & Zegen Zhou & Jia Li & Hongmei Wang & Yilun Liu, 2024. "Enhancing Urban Land Use Identification Using Urban Morphology," Land, MDPI, vol. 13(6), pages 1-31, May.
  • Handle: RePEc:gam:jlands:v:13:y:2024:i:6:p:761-:d:1404034
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    References listed on IDEAS

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    1. Lang, Wei & Long, Ying & Chen, Tingting, 2018. "Rediscovering Chinese cities through the lens of land-use patterns," Land Use Policy, Elsevier, vol. 79(C), pages 362-374.
    2. Chen, Xin & Yu, Le & Du, Zhenrong & Liu, Zhu & Qi, Yuan & Liu, Tao & Gong, Peng, 2022. "Toward sustainable land use in China: A perspective on China’s national land surveys," Land Use Policy, Elsevier, vol. 123(C).
    3. Zhang, Zuo & Tang, Wenwu, 2023. "Mixed landform with high-rise buildings: A spatial analysis integrating horizon-vertical dimension in natural-human urban systems," Land Use Policy, Elsevier, vol. 132(C).
    4. Liu, Yilun & Zhu, A-Xing & Wang, Jingli & Li, Wenkai & Hu, Guohua & Hu, Yueming, 2019. "Land-use decision support in brownfield redevelopment for urban renewal based on crowdsourced data and a presence-and-background learning (PBL) method," Land Use Policy, Elsevier, vol. 88(C).
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    Cited by:

    1. Miaoyi Li & Ningrui Zhu, 2024. "Research on the Method of Artificial Intelligence for Identifying Urban Land-Use Types Based on Areas of Interest (AOI) and Multi-Source Data," Land, MDPI, vol. 13(12), pages 1-17, November.
    2. Xiaojin Huang & Ran Cheng & Jun Wu & Wenjian Yang & Longhao Zhang & Pengbo Li & Wenzhe Zhu, 2024. "Extracting Meso- and Microscale Patterns of Urban Morphology Evolution: Evidence from Binhai New Area of Tianjin, China," Land, MDPI, vol. 13(11), pages 1-27, October.
    3. Aleksandra Milovanović & Nikola Cvetković & Uroš Šošević & Stefan Janković & Mladen Pešić, 2024. "Synergies Between Land Use/Land Cover Mapping and Urban Morphology: A Review of Advances and Methodologies," Land, MDPI, vol. 13(12), pages 1-28, December.

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