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Spatial Analysis of Urban Historic Landscapes Based on Semiautomatic Point Cloud Classification with RandLA-Net Model—Taking the Ancient City of Fangzhou in Huangling County as an Example

Author

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  • Jiaxuan Wang

    (School of Landscape Architecture, Beijing Forestry University, No. 35 Qinghua East Road, Haidian District, Beijing 100083, China)

  • Yixi Gu

    (School of Landscape Architecture, Beijing Forestry University, No. 35 Qinghua East Road, Haidian District, Beijing 100083, China)

  • Xinyi Su

    (School of Landscape Architecture, Beijing Forestry University, No. 35 Qinghua East Road, Haidian District, Beijing 100083, China)

  • Li Ran

    (School of Landscape Architecture, Beijing Forestry University, No. 35 Qinghua East Road, Haidian District, Beijing 100083, China)

  • Kaili Zhang

    (School of Landscape Architecture, Beijing Forestry University, No. 35 Qinghua East Road, Haidian District, Beijing 100083, China)

Abstract

Under the synergy of urban heritage conservation and regional cultural continuity, this study explores the spatial features of “mausoleum–city symbiosis” landscapes in Huangling County’s gully regions. Focusing on Fangzhou Ancient City, we address historical spatial degradation caused by excessive industrialization and disordered urban expansion. A methodological framework is proposed, combining low-altitude UAV-derived high-density point cloud data with RandLA-Net for semi-automatic semantic segmentation of buildings, vegetation, and roads by integrating multispectral and geometric attributes. Key findings reveal: (1) Modern buildings’ abnormal elevation in steep slopes disrupts the plateau–city visual corridor; (2) Statistical analysis shows significant morphological disparities between historical and modern streets; (3) Modern structures exceed traditional height limits, while divergent roof slopes aggravate aesthetic fragmentation. This multi-level spatial analysis offers a paradigm for quantifying historical urban spaces and validates deep learning’s feasibility in heritage spatial analytics, providing insights for balancing conservation and development in ecologically fragile areas.

Suggested Citation

  • Jiaxuan Wang & Yixi Gu & Xinyi Su & Li Ran & Kaili Zhang, 2025. "Spatial Analysis of Urban Historic Landscapes Based on Semiautomatic Point Cloud Classification with RandLA-Net Model—Taking the Ancient City of Fangzhou in Huangling County as an Example," Land, MDPI, vol. 14(6), pages 1-32, May.
    • Handle: RePEc:gam:jlands:v:14:y:2025:i:6:p:1156-:d:1665676
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    References listed on IDEAS

    as
    1. Jianqiao Sun & Long Shao, 2024. "Interpretation of Historic Urban Landscape Genes: A Case Study of Harbin, China," Land, MDPI, vol. 13(12), pages 1-27, November.
    2. He, Qingsong & Larkham, Peter & Wu, Jiayu, 2021. "Evaluating historic preservation zoning using a landscape approach," Land Use Policy, Elsevier, vol. 109(C).
    3. Jun Jiang & Tiancheng Zhou & Yirui Han & Konomi Ikebe, 2022. "Urban Heritage Conservation and Modern Urban Development from the Perspective of the Historic Urban Landscape Approach: A Case Study of Suzhou," Land, MDPI, vol. 11(8), pages 1-20, August.
    4. Zhe Li & Tianlian Wang & Su Sun, 2024. "Research on Quantitative Analysis Methods for the Spatial Characteristics of Traditional Villages Based on Three-Dimensional Point Cloud Data: A Case Study of Liukeng Village, Jiangxi, China," Land, MDPI, vol. 13(8), pages 1-26, August.
    5. Xinpeng Li & Wei Hou & Meng Liu & Zhenlin Yu, 2022. "Traditional Thoughts and Modern Development of the Historical Urban Landscape in China: Lessons Learned from the Example of Pingyao Historical City," Land, MDPI, vol. 11(2), pages 1-19, February.
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    Full references (including those not matched with items on IDEAS)

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