Generative Adversarial Networks (GANs), and Architecture: Investigating Quality in Architectural Plan Generation

Artificial intelligence (AI) finds extensive applications in architecture, alongside various other domains of daily life. Recent years have witnessed a surge in visual processing, analysis, and production, primarily propelled by deep learning algorithms. Among these algorithms, Generative Adversarial Networks (GANs) stand out as exemplary tools for image generation. Within architecture, GANs are utilized across various domains including facade design, interior layout, and generation of perspectives and architectural plans. Notably, GANs have emerged as prominent tools in architectural plan generation. However, unlike other image synthesis tasks, architectural plan generation places greater emphasis on plan quality over image fidelity. Consequently, evaluating the quality of plans generated through AI poses a novel and contemporary challenge. While some studies touch upon quality issues in GAN-generated outputs, a comprehensive exploration of quality-related concerns remains lacking. The study analyses the plans generated by GAN and assesses the capacity of GAN in ensuring architectural quality. To this purpose, a study is undertaken to analyze existing architectural plan generation studies utilizing GANs and to interpret the notion of architectural quality. The studies analyzed that the experiments with GANs are preliminary and there is a lack of studies on the production of higher quality plans using GANs. However, these studies seem to be due to the limitations of GAN itself. This study concludes by underlining the limited capacity of the GANs to enhance the quality of architectural plans, and provides comments and reviews on this matter.