Automatic generation of architecture facade for historical urban renovation using generative adversarial network

In architecture, urban culture inheritance plays a nontrivial role in the urban renovation of a city. The existing design workflow for architects is laborious from the research phase to the final plans, and yet the results are subjective, especially when facing a large-scale urban renovation. Recent works show that generative adversarial networks (GANs) have surprising potential for creating un-presented images following certain rules under various domains, including architecture. However, research on adopting GANs to optimize the conventional design process of historic urban areas renovation is still missing. This study contributes as a decision support tool for urban renovation based on GANs to abstract historic architecture styles and automatically generate stylized facades. One self-made dataset of facade and label information from Harbin Central Street was created, and one data augmentation process was proposed and examined. The generated designs were evaluated quantitatively and qualitatively, and demonstrated high accuracy, reality, and diversity levels. Two in situ and off-site applications proved the feasibility and adaptability of the proposed workflow. Through all evaluations and application cases, it is justified that the proposed GAN-based design strategy can greatly benefit the conventional historic urban area renovation design process.