Localizing Local Vulnerabilities in Urban Areas Using Crowdsourced Visual Data from Participatory Sensing

Hongjo Kim, Youngjib Ham, Hyoungkwan Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

An essential prerequisite for reducing natural disaster damage is to identify objects vulnerable to extreme weather events. However, it is not trivial to scrutinize large urban areas within a short period of time, using conventional data collection processes for disaster preparedness. To address this issue, we propose a novel geospatial localization method building on participatory sensing to localize vulnerable objects or areas in cities. The proposed method consists of sequential modules-a geographic coordinate conversion, mean-shift clustering, deep learning-based object detection, magnetic declination adjustment, line of sight equation formulation, and the Moore-Penrose generalized inverse method-to localize urban objects in crowdsourced data. The localization accuracy of the proposed method is evaluated in a case study of urban areas in Texas. The proposed method is expected to contribute to rapid data collection practice in disaster preparedness and enable practitioners to concentrate their limited resources on where focus is needed.

Original languageEnglish
Title of host publicationComputing in Civil Engineering 2019
Subtitle of host publicationData, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019
EditorsYong K. Cho, Fernanda Leite, Amir Behzadan, Chao Wang
PublisherAmerican Society of Civil Engineers (ASCE)
Pages522-529
Number of pages8
ISBN (Electronic)9780784482438
Publication statusPublished - 2019
EventASCE International Conference on Computing in Civil Engineering 2019: Data, Sensing, and Analytics, i3CE 2019 - Atlanta, United States
Duration: 2019 Jun 172019 Jun 19

Publication series

NameComputing in Civil Engineering 2019: Data, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019

Conference

ConferenceASCE International Conference on Computing in Civil Engineering 2019: Data, Sensing, and Analytics, i3CE 2019
CountryUnited States
CityAtlanta
Period19/6/1719/6/19

Bibliographical note

Funding Information:
This material is in part based upon work supported by the National Science Foundation (NSF) under CMMI Award#1832187. In addition, this work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government—the Ministry of Education (No. 2018R1A6A1A08025348) and the Ministry of Science, ICT and Future Planning (No. 2018R1A2B2008600)—and the Yonsei University Research Fund (Yonsei Frontier Lab. Young Researcher Supporting Program) of 2018. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding agencies.

Publisher Copyright:
© 2019 American Society of Civil Engineers.

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Civil and Structural Engineering

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