Social media crowdsourcing for rapid damage assessment following a sudden-onset natural hazard event |
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| Affiliation: | 1. Department of Civil and Environmental Engineering, University of Maryland, Colege Park, MD, USA;2. Glenn L. Martin Institute Professor of Engineering, University of Maryland, College Park, MD, USA;3. School of Traffic & Transportation Engineering, Changsha University of Science and Technology, Changsha, China;1. Research Center for Advanced Science and Technology, The University of Tokyo, Japan;2. Department of Technology Management for Innovation, The University of Tokyo, Japan;3. Research Institute of Economy, Trade, and Industry (RIETI), Japan;1. Department of Finance and Decision Sciences, School of Business, Trinity University, San Antonio, TX 78212, USA;2. Department of Marketing, Information Systems, and Decision Sciences, Anderson School of Management, University of New Mexico, Albuquerque, NM 87131, USA;1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, China;2. Collaborative Innovation Center of Geospatial Technology, Wuhan, 430079, China;3. Faculty of Geomatics, East China University of Technology, Nanchang, 330013, China;1. DII, Polytechnic University of Marche, Italy;2. DIII, University of Pavia, Italy |
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| Abstract: | Rapid appraisal of damages related to hazard events is of importance to first responders, government agencies, insurance industries, and other private and public organizations. While satellite monitoring, ground-based sensor systems, inspections and other technologies provide data to inform post-disaster response, crowdsourcing through social media is an additional and novel data source. In this study, the use of social media data, principally Twitter postings, is investigated to make approximate but rapid early assessments of damages following a disaster. The goal is to explore the potential utility of using social media data for rapid damage assessment after sudden-onset hazard events and to identify insights related to potential challenges. This study defines a text-based damage assessment scale for earthquake damages, and then develops a text classification model for rapid damage assessment. Although the accuracy remains a challenge compared to ground-based instrumental readings and inspections, the proposed damage assessment model features rapidity with large amounts of data at spatial densities that exceed those of conventional sensor networks. The 2019 Ridgecrest, California earthquake sequence is investigated as a case study. |
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| Keywords: | Sudden-onset hazard Damage assessment Social media Crowdsourcing Text classification |
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