Developing Human-Centered Urban Digital Twins for Community Infrastructure Resilience: A Research Agenda

Abstract

Urban digital twins (UDTs) have been identified as a potential technology to achieve digital transformative positive urban change through landscape architecture and urban planning. However, how this new technology will influence community resilience and adaptation planning is currently unclear. This article: (1) offers a scoping review of existing studies constructing UDTs, (2) identifies challenges and opportunities of UDT technologies for community adaptation planning, and (3) develops a conceptual framework of UDTs for community infrastructure resilience. This article highlights the need for integrating multi-agent interactions, artificial intelligence, and coupled natural–physical–social systems into a human-centered UDTs framework to improve community infrastructure resilience.

3D visualization of hurricane storm surge impact on urban infrastructure

“…The advent of smart cities has facilitated a richer, real-time data influx and enhanced technical support for urban flood simulations. However, visualizing the repercussions of urban flooding on infrastructure and buildings remains a formidable challenge, primarily due to the volume and sheer amount of data now available as well as intricate 3D computational needs and options for modeling (Cai et al, 2023;Ye et al, 2023).…”

The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities: A comprehensive systematic review

“…By leveraging smart IoT sensors, advanced data analytics, powerful AI algorithms, and innovative visualization methods, UDT integrates vast and diverse data from multiple sources to facilitate real-time monitoring and improve predictions and decision-making in urban planning. Indeed, AI and AIoT technologies have recently found their way into the computational functionalities of UDT [ 29 , 30 ], enriching data-driven environmental planning initiatives [ [31] , [32] , [33] , [34] , [35] , [36] ] in sustainable smart cities. By integrating AI models, such as machine learning (ML), deep learning (DL), computer vision (CV), and natural language processing (NLP), planners can effectively manage vast datasets, identify patterns, and discern trends via UDT systems, thereby facilitating more informed decision-making across various domains through automation, optimization, and prediction.…”

Understanding Urban Green Spaces Typology’s Contribution to Comprehensive Green Infrastructure Planning: A Study of Canberra, the National Capital of Australia

“…Preparing an inventory of UGS types and mapping them can facilitate the design of a comprehensive regulatory framework to guide interventions and decisions [13,14]. Moreover, developing a comprehensive UGS typology can serve as a basis for engaging the public in the decision making and planning process through methods such as Public Participation GIS [12] or City Digital Twin [15]. Typology helps to put the available data into distinct categories to standardise the landscape and provide some pragmatic solutions for landscape management and planning [16].…”