Hurricane Ready Cloud (HRC): an ecosystem of connected sensors, data, tools, and users for resilient coastal communities
Creators: name: Guo, Mengyang
Publisher: name: No Publisher Supplied
Year: 2021

description: Extreme weather events such as tropical storms cause significant economic losses, and many coastal communities in the United States and worldwide are facing elevated climate risk due to sea-level rise, continued urbanization, and an increasingly aggressive climate system. In recent years, big spatial data acquisition technologies such as drones and vehicles equipped with high-resolution cameras and LiDAR have been increasingly deployed in disaster management applications, generating an ever larger volume of spatial data that are becoming new sources of valuable information in hazard research. In spite of these great values, the vast size, complex processing requirements of these data sets, and lack of dedicated data infrastructure make it very challenging to effectively use these emerging data sets in hazard research. On top of these computational related limitations, there is also a general lack of understanding on the use cases of these emerging data sets. Lastly, these data sets are currently residing in the domain of academic use, falling short of being utilized in the citizen science/engineering sphere. The latter has the potential to transform risk communication in community resilience. Because of all these limitations, today’s hazard research in the era of harnessing big data revolution still face the challenge of “data rich and information poor”.To address these problems, the Hurricane Ready Cloud (HRC), was developed to systematically organize and synthesize multiple disaster-related big spatial data sets, and to provide artificial intelligence assisted disaster data analysis tools for facilitating hazard assessment tasks and accelerating knowledge discovery from large spatial disaster data sets. More specifically, this study first designed a data infrastructure that supports collection, sharing, visualization, mining, and long-term storage of perishable disaster data in a cloud-based computing environment with an ultimate goal of building better baseline data for risk mitigation and creating a rich digital environment for resilience centered knowledge discovery. Data captured from Hurricane Sandy and Hurricane Harvey were used as case studies to validate the proposed data infrastructure. Then, based on this data infrastructure, this study investigated two major use cases in large-scale home elevation data extraction for flood risk analysis and virtual damage assessment. The Ocean County mobile LiDAR data, a unique county-scale 3D digital elevation data set, was used as the testing data set for elevation extraction and flood risk analysis. Then the mobile LiDAR data collected in Texas in the aftermath of Hurricane Harvey as the testing data set for virtual damage assessment. The overall contribution of this research is designing and validating a cloud-based disaster data infrastructure and validating its unified framework for major use cases such as rapid flood risk analysis and intelligent damage assessment in the aftermath of major hurricane events, and beyond. It resulted in a state-of-the-art cyberinfrastructure for handling large spatial disaster data and for knowledge discovery from these large data sets.