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. 

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Project Team: George Guida, Dongyun Kim

 

Harvard Graduate School of Design, advised by Jose Luis Garcia del Castillo Lopez

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CAADRIA 2022 Conference Paper Link

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