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Generative adversarial networks for ensemble projections of future urban morphology

Authors:

Melissa R. Allen-Dumas,

Abigail R. Wheelis,

Levi T. Sweet-Breu,

Joshua Anantharaj,

Kuldeep R. KurteAuthors Info & Claims

ARIC '22: Proceedings of the 5th ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities

Pages 1 - 6

https://doi.org/10.1145/3557916.3567819

Published: 01 November 2022 Publication History

Abstract

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.

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Cited By

Pasini M(2024)A Perspective on Scalable AI on High-Performance Computing and Leadership Class Supercomputing Facilities [Industrial and Governmental Activities]IEEE Computational Intelligence Magazine10.1109/MCI.2024.340277019:3(6-8)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/MCI.2024.3402770
Xu HOmitaomu FSabri SZlatanova SLi XSong Y(2024)Leveraging generative AI for urban digital twins: a scoping review on the autonomous generation of urban data, scenarios, designs, and 3D city models for smart city advancementUrban Informatics10.1007/s44212-024-00060-w3:1Online publication date: 14-Oct-2024
https://doi.org/10.1007/s44212-024-00060-w

Index Terms

Generative adversarial networks for ensemble projections of future urban morphology
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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cover image ACM Conferences

ARIC '22: Proceedings of the 5th ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities

November 2022

30 pages

ISBN:9781450395304

DOI:10.1145/3557916

Editors:
Bandana Kar
Oak Ridge National Laboratory, TN
,
Shima Mohebbi
George Mason University, VA
,
Guangtao Fu
University of Exeter, Exeter, England
,
Xinyue Ye
Texas A&M University, TX
,
Olufemi A. Omitaomu
Oak Ridge National Laboratory, TN

Copyright © 2022 ACM.

Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 November 2022

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U.S. Department of Energy

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SIGSPATIAL '22

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SIGSPATIAL

SIGSPATIAL '22: The 30th International Conference on Advances in Geographic Information Systems

November 1, 2022

Washington, Seattle

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Cited By

Pasini M(2024)A Perspective on Scalable AI on High-Performance Computing and Leadership Class Supercomputing Facilities [Industrial and Governmental Activities]IEEE Computational Intelligence Magazine10.1109/MCI.2024.340277019:3(6-8)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/MCI.2024.3402770
Xu HOmitaomu FSabri SZlatanova SLi XSong Y(2024)Leveraging generative AI for urban digital twins: a scoping review on the autonomous generation of urban data, scenarios, designs, and 3D city models for smart city advancementUrban Informatics10.1007/s44212-024-00060-w3:1Online publication date: 14-Oct-2024
https://doi.org/10.1007/s44212-024-00060-w

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