Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Research on the Application of CGAN in the Design of Historic Building Facades in Urban Renewal—Taking Fujian Putian Historic Districts as an Example

Version 1 : Received: 28 April 2023 / Approved: 3 May 2023 / Online: 3 May 2023 (14:50:45 CEST)

A peer-reviewed article of this Preprint also exists.

Lin, H.; Huang, L.; Chen, Y.; Zheng, L.; Huang, M.; Chen, Y. Research on the Application of CGAN in the Design of Historic Building Facades in Urban Renewal—Taking Fujian Putian Historic Districts as an Example. Buildings 2023, 13, 1478. Lin, H.; Huang, L.; Chen, Y.; Zheng, L.; Huang, M.; Chen, Y. Research on the Application of CGAN in the Design of Historic Building Facades in Urban Renewal—Taking Fujian Putian Historic Districts as an Example. Buildings 2023, 13, 1478.

Abstract

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 districts, which will aid in the design and control of district 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 interpreting image data of historical district 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 districts. 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 district reconstruction, facade renovation, and renovation design projects. Especially for districts 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 district 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 improve the efficiency and ability of professional design.

Keywords

machine learning; conditional generative adversarial network (CGAN); historic district; facade design; decoration style; urban renewal

Subject

Engineering, Architecture, Building and Construction

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