Research on the Application of CGAN in the Design of Historic Building Facades in Urban Renewal—Taking Fujian Putian Historic Districts as an Example(Training set for machine learning)

This dataset is about the paper "Research on the Application of CGAN in the Design of Historic Building Facades in Urban Renewal—Taking Fujian Putian Historic Districts as an Example".In recent years, artificial intelligence technology has widely influenced the field of design, bringing new ideas to efficiently and systematically solve urban renewal design problems. The purpose of this study is to create a stylized generation technology for building facade decoration in historic blocks, which will aid in the design and control of block style and form. The goal is to use the technical advantages of conditional generative adversarial network (CGAN) in image generation and style transfer to create a method for independently designing a specific facade decoration style by in-terpreting image data of historical block facades. The research in this paper is based on the historical district of Putian in Fujian Province, through an experiment of image data acquisition, image processing and screening, model training, image generation, and style matching of the target area. The research found that: (1) CGAN technology can better identify and generate the decorative style of historical blocks. It can realize the overall or partial scheme design of the facade; (2) in terms of adaptability, this method can provide a better scheme reference for historical block reconstruction, facade renovation, and renovation design projects. Especially for blocks with obvious decorative styles, the visualization effect is better. In addition, it also has certain reference significance for the determination and design of the facade decoration style of a specific historical building; (3) This method can better learn the internal laws of the complex block style and form so as to generate a new design with a clear decoration style attribute. It can be extended to other fields of historical heritage protection to enhance practitioners' stylized control of the heritage environment and im-prove the efficiency and ability of professional design.本数据集关联论文《城市更新中CGAN在历史建筑立面设计中的应用——以福建莆田历史街区为例》。近年来，人工智能技术已广泛渗透至设计领域，为高效、系统地解决城市更新设计难题提供了全新思路。本研究旨在构建历史街区建筑立面装饰的风格化生成技术，以辅助街区风貌与形态的设计管控。本研究的目标是依托条件生成对抗网络（Conditional Generative Adversarial Network，简称CGAN）在图像生成与风格迁移领域的技术优势，通过解析历史街区建筑立面的图像数据，打造可独立设计特定立面装饰风格的方法。本文的研究以福建省莆田历史街区为研究载体，通过对目标区域开展图像数据采集、图像处理与筛选、模型训练、图像生成及风格匹配等一系列实验完成。研究结果表明：其一，CGAN技术可较好地识别并生成历史街区的装饰风格，能够实现建筑立面的整体或局部方案设计；其二，从适配性来看，该方法可为历史街区改造、立面翻新及改造设计项目提供优质方案参考，尤其在装饰风格特征显著的街区中可视化效果更佳。此外，其对于特定历史建筑的立面装饰风格确定与设计亦具备一定参考价值；其三，该方法可更好地学习复杂街区风貌与形态的内在规律，从而生成具备明确装饰风格属性的全新设计方案。该方法可拓展至历史遗产保护的其他领域，助力从业者强化对遗产环境的风格化管控，提升专业设计的效率与能力。