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Digital Twin City for Enhanced Data Mapping and Virtuality-Reality Connectivity: Toward Risk-Informed Decision Making
Abstract
Hurricane is one of the most devastating natural hazards creating damages more than billions of dollars every year. During extreme weather-related hazards such as floods, coastal communities are more susceptible because they consist of urban environments with a high concentration of assets and populations, which results in critical economic loss and social disruptions in neighboring communities. To address potential vulnerability of urban infrastructure systems under extreme weather conditions, governments have been building the 3D model of city infrastructure systems. In the model, the geospatial information can be helpful in identifying and reporting damages or hazards of critical infrastructure systems for disaster response and recovery. Despite the benefits of GIS data of infrastructure systems for disaster management, some of such data currently in use is often outdated or missing. Furthermore, they often have not fully covered the geospatial and up-to-date condition information of urban infrastructure due to the limited resources, which may cause challenges in risk-informed decision making. To address such challenges, participatory sensing and publicly available large-scale street view imagery have been emerged as a rich source of data in urban areas to identify and analyze the local vulnerability of the city infrastructure system toward the digital twin city. The proposed method is described in three main steps as follows: 1) collecting the participatory sensing and publicly available large-scale visual data, 2) analyzing the sensing data to identify the geospatial (i.e., the estimated location of distant local vulnerability) and up-to-date infrastructure condition information (e.g., leaning angle of a utility pole, clogged area of inlet), 3) performing the risk assessment associated with local vulnerability with respect to the severity of hurricane events, 4) building a cloud computing-based 3D digital twin city model to update the data and analysis in real-time. Considering the convenience of visual data collection, as well as the advancements in computer vision-based frameworks, the updated 3D virtual city model through interactive visualization is expected to contribute to risk-informed decision-makings in cities, which helps analyze various what-if scenarios in disaster situations with the increased visibility into hazard and city interactions.
Citation
Kim, Jaeyoon (2023). Digital Twin City for Enhanced Data Mapping and Virtuality-Reality Connectivity: Toward Risk-Informed Decision Making. Doctoral dissertation, Texas A&M University. Available electronically from https : / /hdl .handle .net /1969 .1 /199781.