Rapid Disaster Data Dissemination and Vulnerability Assessment through Synthesis of a Web-Based Extreme Event Viewer and Deep Learning

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dc.contributor.author Crawford, P. Shane
dc.contributor.author Al-Zarrad, Mohammad A.
dc.contributor.author Graettinger, Andrew J.
dc.contributor.author Hainen, Alexander M.
dc.contributor.author Back, Edward
dc.contributor.author Powell, Lawrence
dc.date.accessioned 2021-06-23T20:02:16Z
dc.date.available 2021-06-23T20:02:16Z
dc.date.issued 2018-11-13
dc.identifier.citation Crawford, P., Al-Zarrad, M., Graettinger, A., Hainen, A., Back, E., Powell, L. (2018): Rapid Disaster Data Dissemination and Vulnerability Assessment through Synthesis of a Web-Based Extreme Event Viewer and Deep Learning. Advances in Civil Engineering, Volume 2018. en_US
dc.identifier.uri http://ir.ua.edu/handle/123456789/7809
dc.description.abstract Infrastructure vulnerability has drawn significant attention in recent years, partly because of the occurrence of low-probability and high-consequence disruptive events such as 2017 hurricanes Harvey, Irma, and Maria, 2011 Tuscaloosa and Joplin tornadoes, and 2015 Gorkha, Nepal, and 2017 Central Mexico earthquakes. Civil infrastructure systems support social welfare, thus viability and sustained operation is critical. A variety of frameworks, models, and tools exist for advancing infrastructure vulnerability research. Nevertheless, providing accurate vulnerability measurement remains challenging. ,is paper presents a state-of-the-art data collection and information extraction methodology to document infrastructure at high granularity to assess preevent vulnerability and postevent damage in the face of disasters. ,e methods establish a baseline of preevent infrastructure functionality that can be used to measure impacts and temporal recovery following a disaster. ,e Extreme Events Web Viewer (EEWV) presented as part of the methodology is a GIS-based web repository storing spatial and temporal data describing communities before and after disasters and facilitating data analysis techniques. ,is web platform can store multiple geolocated data formats including photographs and 360° videos. A tool for automated extraction of photography from 360° video data at locations of interest specified in the EEWV was created to streamline data utility. ,e extracted imagery provides a manageable data set to efficiently document characteristics of the built and natural environment. ,e methodology was tested to locate buildings vulnerable to flood and storm surge on Dauphin Island, Alabama. Approximately 1,950 buildings were passively documented with vehicle-mounted 360° video. Extracted building images were used to train a deep learning neural network to predict whether a building was elevated or nonelevated. ,e model was validated, and methods for iterative neural network training are described. ,e methodology, from rapidly collecting large passive datasets, storing the data in an open repository, extracting manageable datasets, and obtaining information from data through deep learning, will facilitate vulnerability and postdisaster analyses as well as longitudinal recovery measurement. en_US
dc.description.uri https://doi.org/10.1155/2018/7258156
dc.format.mimetype application/pdf
dc.language English en_US
dc.relation.uri https://creativecommons.org/licenses/by/4.0/
dc.subject Infrastructure vulnerability en_US
dc.title Rapid Disaster Data Dissemination and Vulnerability Assessment through Synthesis of a Web-Based Extreme Event Viewer and Deep Learning en_US
dc.type text


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