Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu Earthquake
Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu Earthquake
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基于航空遥感影像的震后倒塌建筑众包快速评估——以玉树地震为例
DOI：
10.3390/rs8090759
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Caihong Ma
中科院分区：
文献类型：
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作者：
Shuai Xie;Jianbo Duan;Shibin Liu;Q. Dai;W. Liu;Yong Ma;Rui Guo;Caihong Ma
遥感图像在灾害应急响应中发挥着重要作用。Web2.0改变了数据创建的方式，使公众参与科学问题成为可能。本文设计了一个基于航空遥感图像的震后早期建筑物倒塌评估的实验，以评估众包的可靠性。介绍了遥感数据预处理和众包数据采集的流程。采用概率模型，包括最大似然估计（MLE）、贝叶斯定理和期望最大化（EM）算法，根据多个参与者的评估结果定量估计个体错误率和“地面真实值”。以玉树地震为例，介绍了参与者的研究成果.在结果之后，提供了一些关于参与者之间的准确性和变化的讨论。同一损伤类型的建筑物的特征具有高度的一致性。这表明，众包贡献的建筑物损坏评估可以被视为可靠的样本。这项研究显示了潜在的快速建筑物倒塌评估，通过众包和定量推断“地面真相”，根据众包数据在地震后的早期时间的基础上航空遥感图像。
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.