PlacemakingAI : Participatory Urban Design with Generative Adversarial Networks

Abstract

Machine Learning (ML) is increasingly present within the architectural discipline, expanding the current possibilities of procedural computer-aided design processes. Practical 2D design applications used within concept design stages are however limited by the thresholds of entry, output image fidelity, and designer agency. This research proposes to challenge these limitations within the context of urban planning and make the design processes accessible and collaborative for all urban stakeholders. We present PlacemakingAI, a design tool made to envision sustainable urban spaces. By converging supervised and unsupervised Generative Adversarial Networks (GANs) with a real-time user interface, the decision-making process of planning future urban spaces can be facilitated. Several metrics of walkability can be extracted from curated Google Street View (GSV) datasets when overlayed on existing street images. The contribution of this framework is a shift away from traditional design and visualization processes, towards a model where multiple design solutions can be rapidly visualized as synthetic images and iteratively manipulated by users. In this paper, we discuss the convergence of both a generative image methodology and this real-time urban prototyping and visualization tool, ultimately fostering engagement within the urban design process for citizens, designers, and stakeholders alike.