Mapping Local Vulnerabilities into a 3D City Model through Social Sensing and the CAVE System toward Digital Twin City

Understanding the states of spatiotemporal flux in cities is critical for risk-informed decision making in disaster situations, resulting in crucial influences on physical, environmental, and social systems of cities. This paper presents a new framework to better understand dynamic spatiotemporal fluctuations related to the vulnerability of cities. We first leverage crowdsourced visual data to automatically identify and localize potential risks associated with vulnerable objects in cities before/during/after disasters. Then we bring multimodal sensing-based reality information into a 3D city model and an interactive computer-aided virtual environment (CAVE), which facilitates to interact the spatial information of vulnerable objects at the intersection of reality-virtuality of cities. For evaluation, case studies were conducted on Houston, TX. The resulting digital twin city reflecting multimodal sensing-based vulnerability information of cities has the potential to be used as a basis for simulating what-if scenarios for risk-informed decision making in disaster situations with enhanced reliability of analytics.