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首页> 外文期刊>International journal of information systems for crisis response and management >Assessment of the Contribution of Crowd Sourced Data to Post-Earthquake Building Damage Detection
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Assessment of the Contribution of Crowd Sourced Data to Post-Earthquake Building Damage Detection

机译：评估人群数据对震后建筑物损坏检测的贡献

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摘要

This article compares the results of building damage detection based on Crowd Sourced (CS) data, image processing of remotely sensed (RS) data and predictive modelling with institutional spatial data (Spatial Data Infrastructure - SDI). In particular, it focuses on the contribution of Crowd Sourcing to detecting post-earthquake building damages, while also considering the integration of Crowd Sourced with two other data sources (RS and modelling). To simulate CS data submission following the 2003 earthquake in Bam City (Iran) a survey was administered to the population which experienced the earthquake. The results obtained from this and two other sources are compared with the Actual Earthquake (AE) data by cross-tabulation analysis and McNemar's Chi Square Test. When assessed against AE data, the average accuracy levels of assessments based on the use of RS data and CS data integrated with each RS data and predictive modelling and with both, show a statistically significant increase relative to the predictive modelling. While this research does not provide for a full assessment of the value of CS data alone and in fact finds it slightly inferior to predictive modelling, it suggests that Crowd Sourcing could be a useful source of information, especially if combined with other sources.
机译：本文比较了基于众包（CS）数据，遥感（RS）数据的图像处理以及具有机构空间数据（Spatial Data Infrastructure-SDI）的预测建模的建筑物损坏检测的结果。特别是，它着重于人群搜索对检测地震后建筑物破坏的贡献，同时还考虑了将人群来源与其他两个数据源（RS和建模）集成。为了模拟2003年巴姆市（伊朗）地震后的CS数据提交，对经历地震的人口进行了调查。通过交叉列表分析和McNemar的卡方检验，将从该来源和其他两个来源获得的结果与实际地震（AE）数据进行比较。当根据AE数据进行评估时，基于使用RS数据和CS数据（与每个RS数据和预测性建模以及二者结合）的评估的平均准确度水平相对于预测性建模显示出统计学上的显着提高。虽然这项研究并不能单独对CS数据的价值进行全面评估，并且实际上发现它比预测模型稍差一些，但它表明，Crowd Sourcing可能是有用的信息来源，尤其是与其他来源结合使用时。

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