  The digital transformation of modern cities by integrating advanced
information, communication, and computing technologies has marked the epoch of
data-driven smart city applications for efficient and sustainable urban
management. Despite their effectiveness, these applications often rely on
massive amounts of high-dimensional and multi-domain data for monitoring and
characterizing different urban sub-systems, presenting challenges in
application areas that are limited by data quality and availability, as well as
costly efforts for generating urban scenarios and design alternatives. As an
emerging research area in deep learning, Generative Artificial Intelligence
(AI) models have demonstrated their unique values in data and code generation.
This survey paper aims to explore the innovative integration of generative AI
techniques and urban digital twins to address challenges in the realm of smart
cities in various urban sectors, such as transportation and mobility
management, energy system operations, building and infrastructure management,
and urban design. The survey starts with the introduction of popular generative
AI models with their application areas, followed by a structured review of the
existing urban science applications that leverage the autonomous capability of
the generative AI techniques to facilitate (a) data augmentation for promoting
urban monitoring and predictive analytics, (b) synthetic data and scenario
generation, (c) automated 3D city modeling, and (d) generative urban design and
optimization. Based on the review, this survey discusses potential
opportunities and technical strategies that integrate generative AI models into
the next-generation urban digital twins for more reliable, scalable, and
automated management of smart cities.
