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Generative adversarial networks for ensemble projections of future urban morphology

by Melissa R Dumas, Abigail Wheelis, Levi T Sweet-breu, Joshua Anantharaj, Kuldeep R Kurte
Publication Type
Conference Paper
Book Title
ARIC '22: Proceedings of the 5th ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities
Publication Date
Page Numbers
1 to 6
Publisher Location
New York, United States of America
Conference Name
The 5th ACM SIGSPATIAL Workshop on Advances in Resilient and Intelligent Cities (ARIC 2022)
Conference Location
Seattle, Washington, United States of America
Conference Sponsor
Conference Date

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.