Abstract
Due to their importance in weather and climate assessments, there is significant interest to represent cities in numerical prediction models. However, getting high resolution multi-faceted data about a city has been a challenge. Further, even when the data were available the integration into a model is even more of a challenge due to the parametric needs, and the data volumes. Further, even if this is achieved, the cities themselves continually evolve rendering the data obsolete, thus necessitating a fast and repeatable data capture mechanism. We have shown that by using AI/graphics community advances we can create a seamless opportunity for high resolution models. Instead of assuming every physical and behavioral detail is sensed, a generative and procedural approach seeks to computationally infer a fully detailed 3D fit-for-purpose model of an urban space. We present a perspective building on recent success results of this generative approach applied to urban design and planning at different scales, for different components of the urban landscape, and related applications. the opportunities now possible with such a generative model for urban modeling open a wide range of opportunities as this becomes mainstream.
Recommended Citation
D. Aliaga and D. Niyogi, "Digitizing Cities for Urban Weather: Representing Realistic Cities for Weather and Climate Simulations using Computer Graphics and Artificial Intelligence," Computational Urban Science, vol. 4, no. 1, article no. 8, Springer, Dec 2024.
The definitive version is available at https://doi.org/10.1007/s43762-023-00111-z
Department(s)
Biological Sciences
Publication Status
Open Access
Keywords and Phrases
Artificial intelligence; Generative AI; Urban computational science; Urban modeling
International Standard Serial Number (ISSN)
2730-6852
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Springer, All rights reserved.
Publication Date
01 Dec 2024
Comments
National Science Foundation, Grant 1835739