Construction of a Machine Learning Based Database for Building Facades: A Case Study of Shantou Xiaogongyuan District

Ma Lan; Luo Dajie; Xie Weilin; Tang Zhong; He Shaoying

doi:10.12052/gdutxb.240124

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Construction of a Machine Learning Based Database for Building Facades: A Case Study of Shantou Xiaogongyuan District

更新时间：2026-05-27

- Construction of a Machine Learning Based Database for Building Facades: A Case Study of Shantou Xiaogongyuan District
- Vol. 42, Issue 6, Pages: 126-133(2025)
- 作者机构：
  
  1. 广东工业大学建筑与城市规划学院,广东,广州,510090
  2. 同济大学建筑与城市规划学院,上海,200092
  3. 广东工业大学通识教育中心、文化素质教育中心,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.240124
  CLC： TU17/TP392
- Published：2025
- 稿件说明：
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马兰, 罗大杰, 谢炜琳, 等. 基于机器学习的建筑立面数据库构建——以汕头小公园开埠区为例[J]. 广东工业大学学报, 2025, 42(6): 126–133.

Ma Lan, Luo Dajie, Xie Weilin, et al. Construction of a Machine Learning Based Database for Building Facades: A Case Study of Shantou Xiaogongyuan District[J]. 2025, 42(6): 126-133.
马兰, 罗大杰, 谢炜琳, 等. 基于机器学习的建筑立面数据库构建——以汕头小公园开埠区为例[J]. 广东工业大学学报, 2025, 42(6): 126–133. DOI： 10.12052/gdutxb.240124

Ma Lan, Luo Dajie, Xie Weilin, et al. Construction of a Machine Learning Based Database for Building Facades: A Case Study of Shantou Xiaogongyuan District[J]. 2025, 42(6): 126-133. DOI： 10.12052/gdutxb.240124.

摘要

历史风貌流失、设计修复缺乏依据、社会认知欠缺等是历史文化街区保护中普遍面临的困境。通过数据库对具有历史价值的建筑进行数字记录与分析是信息时代文化遗产传承的有效途径。以广东省汕头小公园开埠区中具有历史风貌的建筑为例，通过对建筑立面元素的数字化编码与机器学习协同训练构建综合性数据库，实现街区中历史风貌建筑案例的检索、风貌评估以及建筑立面原型探索等方面的应用。研究结果不仅为历史文化街区建筑的保护与传承提供了科学路径，也展现了数字技术在建筑文化遗产保护中的巨大应用潜力。

Abstract

The preservation of historical street styles faces challenges due to the lack of design and restoration guidelines and the limitations of social awareness. Creating a digital record and analysis of buildings with historical value offers an effective approach for preserving cultural heritage in the digital era. Taking historically styled buildings in Shantou's Xiaogongyuan district as an example

a comprehensive database is established in this study by digitally encoding facade elements and training machine learning model. This enables applications such as historical style assessment and prototype analysis of building facades. The study not only provides scientific guidance for the conservation and restoration of historical buildings in cultural districts but also highlights the significant potential of digital technology in cultural heritage preservation.

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references

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