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

Abstract

In recent years, artificial intelligence technology has widely influenced the design field, introducing 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 the 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 and facilitates an experiment of image data acquisition, image processing and screening, model training, image generation, and style matching of the target area. The research found the following: (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) Lastly, this method can better learn the internal laws of the complex district style and form 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 capability of professional design.

Keywords

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