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Volume 14
Issue 6
10.3390/land14061156
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

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

by
Jiaxuan Wang
,
Yixi Gu
,
Xinyi Su
,
Li Ran
and
Kaili Zhang
*
School of Landscape Architecture, Beijing Forestry University, No. 35 Qinghua East Road, Haidian District, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Land 2025, 14(6), 1156; https://doi.org/10.3390/land14061156
Submission received: 25 April 2025 / Revised: 24 May 2025 / Accepted: 26 May 2025 / Published: 27 May 2025
Review Reports Versions Notes

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.
Keywords: historic urban landscape; cultural heritage preservation; RandLA-Net; point cloud classification; three-dimensional spatial analysis historic urban landscape; cultural heritage preservation; RandLA-Net; point cloud classification; three-dimensional spatial analysis

Share and Cite

MDPI and ACS Style

Wang, J.; Gu, Y.; Su, X.; Ran, L.; Zhang, K. 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 2025, 14, 1156. https://doi.org/10.3390/land14061156

AMA Style

Wang J, Gu Y, Su X, Ran L, Zhang K. 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. 2025; 14(6):1156. https://doi.org/10.3390/land14061156

Chicago/Turabian Style

Wang, Jiaxuan, Yixi Gu, Xinyi Su, Li Ran, and 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 14, no. 6: 1156. https://doi.org/10.3390/land14061156

APA Style

Wang, J., Gu, Y., Su, X., Ran, L., & Zhang, K. (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, 14(6), 1156. https://doi.org/10.3390/land14061156

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