Digital Transformation of Urban Planning in Australia: Influencing Factors and Key Challenges

Over the past two decades, several governments in developing and developed countries have started their journey toward digital transformation. However, the pace and maturity of digital technologies and strategies are different between public services. Current literature indicates that research on the digital transformation of urban planning is still developing. Therefore, the aim of this study is to understand the influencing factors and key challenges for the digital transformation of urban planning in Australia. The study adopts the inter-organisational theory and Planning Support Science (PSScience) under the Technological, Organisational, and External Environmental (TOE) framework. It involves a multiple case study, administered semi-structured interviews with thirteen IT and urban planning experts across Victoria and New South Wales governments and private industries. The study findings indicate that the main challenges for digital transformation of the Australian urban planning system are related to organisational and external environmental factors. Furthermore, a digital maturity model is absent in the Australian urban planning industry. This study offers important implications to research and practice related to digital transformation in urban planning.

Leveraging Generative AI for Smart City Digital Twins: A Survey on the Autonomous Generation of Data, Scenarios, 3D City Models, and Urban Designs

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.