Increasing the Accuracy of Crowdsourced Information on Land Cover via a Voting Procedure Weighted by Information Inferred from the Contributed Data
Increasing the Accuracy of Crowdsourced Information on Land Cover via a Voting Procedure Weighted by Information Inferred from the Contributed Data
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DOI：
10.3390/ijgi7030080
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发表时间：
2018-03-01
影响因子：
3.4
通讯作者：
Boyd, Doreen
中科院分区：
文献类型：
--
作者：
Foody, Giles;See, Linda;Boyd, Doreen
在众包研究中，当数据由多个贡献者提供时，通常使用简单的共识方法来标记案例。通常采用基本的多数表决规则。这种方法对每个贡献者的贡献进行同等加权，但贡献者标记案例的准确性可能会有所不同。在这里，探讨了通过使用加权投票策略提高从卫星遥感器图像中确定的土地覆盖众包数据的准确性的潜力。重要的是，用于基于贡献者标记类别的情况和类别的相对丰度的准确性来加权贡献的信息完全是通过潜在类别分析从贡献的数据中推断出来的。结果表明，共识方法确实产生了比任何单个贡献者更准确的分类。在这里，最准确的个人可以以73.91%的准确度对数据进行分类，而从所有七名志愿者提供的数据中得出的基本共识标签为76.58%。更重要的是，结果表明，加权贡献可以导致整体准确性在统计上显著增加到80.60%，忽略了被认为是标签中最不准确的志愿者的贡献。
Simple consensus methods are often used in crowdsourcing studies to label cases when data are provided by multiple contributors. A basic majority vote rule is often used. This approach weights the contributions from each contributor equally but the contributors may vary in the accuracy with which they can label cases. Here, the potential to increase the accuracy of crowdsourced data on land cover identified from satellite remote sensor images through the use of weighted voting strategies is explored. Critically, the information used to weight contributions based on the accuracy with which a contributor labels cases of a class and the relative abundance of class are inferred entirely from the contributed data only via a latent class analysis. The results show that consensus approaches do yield a classification that is more accurate than that achieved by any individual contributor. Here, the most accurate individual could classify the data with an accuracy of 73.91% while a basic consensus label derived from the data provided by all seven volunteers contributing data was 76.58%. More importantly, the results show that weighting contributions can lead to a statistically significant increase in the overall accuracy to 80.60% by ignoring the contributions from the volunteer adjudged to be the least accurate in labelling.