Harvard Graduate School of Design
This project proposes a novel method for generating and visualizing walkable urban streets supported by Generative Adversarial Networks (or GANs). The presented images synthesize a collection of urban image data classified through metrics of walkability, including green, commercial, and pedestrian streets. A customized Pix2Pix model (Isola et al. 2016) is trained continuously for 3 days to generate new urban images in real-time. An accessible user-interface is used to create place, where images or 3D camera views of streets can be overlaid and synthesized based on these pre-trained models. By converging both the generative image methodology and a user interface PlacemakingAI fosters an urban design process for citizens, designers, and stakeholders alike.
Project Team: George Guida, Dongyun Kim
Harvard Graduate School of Design, with Jose Luis Garcia del Castillo Lopez
Machine Learning Models
Real - Time User Interface