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来源类型Article
规范类型其他
DOI10.3390/ijgi7030080 (registering DOI)
Increasing the Accuracy of Crowdsourced Information on Land Cover via a Voting Procedure Weighted by Information Inferred from the Contributed Data.
Foody G; See L; Fritz S; Moorthy I; Perger C; Schill D; Boyd D
发表日期2018
出处ISPRS International Journal of Geo-Information 7 (3): p. 80
出版年2018
语种英语
摘要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.
主题Ecosystems Services and Management (ESM)
关键词crowdsourcing volunteered geographic information (VGI) ensemble classification accuracy latent class analysis
URLhttp://pure.iiasa.ac.at/id/eprint/15140/
来源智库International Institute for Applied Systems Analysis (Austria)
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资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/131299
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GB/T 7714
Foody G,See L,Fritz S,et al. Increasing the Accuracy of Crowdsourced Information on Land Cover via a Voting Procedure Weighted by Information Inferred from the Contributed Data.. 2018.
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