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.
|Title of host publication||Computing in Civil Engineering 2019|
|Subtitle of host publication||Data, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019|
|Editors||Yong K. Cho, Fernanda Leite, Amir Behzadan, Chao Wang|
|Publisher||American Society of Civil Engineers (ASCE)|
|Number of pages||8|
|Publication status||Published - 2019|
|Event||ASCE International Conference on Computing in Civil Engineering 2019: Data, Sensing, and Analytics, i3CE 2019 - Atlanta, United States|
Duration: 2019 Jun 17 → 2019 Jun 19
|Name||Computing in Civil Engineering 2019: Data, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019|
|Conference||ASCE International Conference on Computing in Civil Engineering 2019: Data, Sensing, and Analytics, i3CE 2019|
|Period||19/6/17 → 19/6/19|
Bibliographical noteFunding 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.
© 2019 American Society of Civil Engineers.
All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Civil and Structural Engineering