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Publication Date
1 November 2022

Generative Adversarial Networks for the Prediction of Future Urban Morphology



As city planners design and adapt cities for future resilience and intelligence, interactions among neighborhood morphological development with respect to changes in population and resultant built infrastructure's impact on the natural environment must be considered. For deep understanding of these interactions, explicit representation of future neighborhoods is necessary for future city modeling. Generative Adversarial Networks (GANs) have been shown to produce spatially accurate urban forms at scales representing entire cities to those at neighborhood and single building scale. Here we demonstrate a GAN method for generating an ensemble of possible new neighborhoods given land use characteristics and designated neighborhood type.

Allen-Dumas, Melissa R., Abigail R. Wheelis, Levi T. Sweet-Breu, Joshua Anantharaj, and Kuldeep R. Kurte. 2022. “Generative Adversarial Networks For The Prediction Of Future Urban Morphology”. Aric '22: Proceedings Of The 5Th Acm Sigspatial International Workshop On Advances In Resilient And Intelligent Cities. Seattle: Association for Computing Machinery. doi:10.1145/3557916.3567819.
Original Publication:
GAN_UrbMorph.pdf (1.55 MB)
Funding Program Area(s)