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AI-based urban layout generation model
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DOI：10.1038/s42949-026-00369-2.png)
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Abstract
En 中文
Accurate urban geometric layouts are critical for urban planning, simulation, and sustainability management; however, no ideal solution currently exists for large-scale urban geometric layout creation. We propose a generative AI-based urban layout model capable of encoding arbitrary 3D city blocks to a unified latent representation and generating urban layouts for the 330 cities in North America that have over 100,000 inhabitants. Given only a few percentage of the city blocks, our approach is able to generate an entire realistic 3D city, to support street-scale physical simulations, to predict social-economic metrics, and to enable “what-if” scenarios for policy-making. Compared to data-driven approaches, ours provides a unified representation suitable for large-scale urban simulation and policy-making as part of a digital twin framework, which would otherwise require a time- and resource-consuming process of obtaining the position, geometry, and height of all buildings individually.
Keywords:
Engineering
Mathematics and computing
Environment
general
Sustainable Development
Urban Geography / Urbanism (inc. megacities
cities
towns)
Urbanism
Urban Ecology
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