AI-based urban layout generation model

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.