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Remote Sens. 2016, 8(9), 759; doi:10.3390/rs8090759

Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu Earthquake

1
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Academic Editors: Roberto Tomás, Zhenhong Li, Gonzalo Pajares and Prasad S. Thenkabail
Received: 14 July 2016 / Revised: 27 August 2016 / Accepted: 9 September 2016 / Published: 14 September 2016
(This article belongs to the Special Issue Earth Observations for Geohazards)
View Full-Text   |   Download PDF [12684 KB, uploaded 14 September 2016]   |  

Abstract

Remote sensing (RS) images play a significant role in disaster emergency response. Web2.0 changes the way data are created, making it possible for the public to participate in scientific issues. In this paper, an experiment is designed to evaluate the reliability of crowdsourcing buildings collapse assessment in the early time after an earthquake based on aerial remote sensing image. The procedure of RS data pre-processing and crowdsourcing data collection is presented. A probabilistic model including maximum likelihood estimation (MLE), Bayes’ theorem and expectation-maximization (EM) algorithm are applied to quantitatively estimate the individual error-rate and “ground truth” according to multiple participants’ assessment results. An experimental area of Yushu earthquake is provided to present the results contributed by participants. Following the results, some discussion is provided regarding accuracy and variation among participants. The features of buildings labeled as the same damage type are found highly consistent. This suggests that the building damage assessment contributed by crowdsourcing can be treated as reliable samples. This study shows potential for a rapid building collapse assessment through crowdsourcing and quantitatively inferring “ground truth” according to crowdsourcing data in the early time after the earthquake based on aerial remote sensing image. View Full-Text
Keywords: crowdsourcing; building collapse assessment; earthquake; aerial image; EM algorithm crowdsourcing; building collapse assessment; earthquake; aerial image; EM algorithm
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Xie, S.; Duan, J.; Liu, S.; Dai, Q.; Liu, W.; Ma, Y.; Guo, R.; Ma, C. Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu Earthquake. Remote Sens. 2016, 8, 759.

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