Participatory Sensing and Digital Twin City: Updating Virtual City Models for Enhanced Risk-Informed Decision-Making

Abstract

The benefits of a digital twin city have been assessed based on real-time data collected from preinstalled Internet of Things (IoT) sensors (e.g., traffic, energy use, air pollution, water quality) for managing the complex systems of cities, but the sensor-based reality information is likely insufficient to provide dynamic spatiotemporal information about physical vulnerabilities. Understanding cities’ current states of physical vulnerability can support city decision makers in analyzing associated potential risk in urban areas for data-driven infrastructure management in extreme weather events. As a step toward creating a digital twin city for effective risk-informed decision-making, this paper proposes a new framework to bring crowdsourced visual data-based reality information into a three-dimensional (3D) virtual city for a model update with interactive and immersive visualization. Unstructured visual data are collected from participatory sensing and analyzed to estimate the geospatial information of vulnerable objects in the distance representing physical vulnerability in cities. The crowdsourced visual data–based reality information of physical vulnerability in a given region is then integrated with a 3D virtual city model, and the updated 3D city model is fed into a computer-aided virtual environment (CAVE) for immersive visualization to enable users to navigate the intersection of reality and virtuality. To test the proposed framework, case studies were conducted on Houston. The outcomes demonstrate that the proposed method has the potential to make the virtual city model live in terms of local vulnerability. The digital twin city building on crowdsourced visual data is expected to contribute to risk-informed decision-making for infrastructure management in cities and help analyze various what-if scenarios in disaster situations with increased visibility of hazard and city interactions.