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ISPRS Int. J. Geo-Inf. 2018, 7(3), 80; https://doi.org/10.3390/ijgi7030080

Increasing the Accuracy of Crowdsourced Information on Land Cover via a Voting Procedure Weighted by Information Inferred from the Contributed Data

1
School of Geography, University of Nottingham, Nottingham NG7 2RD, UK
2
International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, A-2361 Laxenburg, Austria
3
Faculty of Environment and Natural Resources, Albert-Ludwig University, 79085 Freiburg, Germany
*
Author to whom correspondence should be addressed.
Received: 22 January 2018 / Revised: 16 February 2018 / Accepted: 21 February 2018 / Published: 25 February 2018
(This article belongs to the Special Issue Geoinformatics in Citizen Science)
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Abstract

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. View Full-Text
Keywords: crowdsourcing; volunteered geographic information (VGI); ensemble; classification accuracy; latent class analysis crowdsourcing; volunteered geographic information (VGI); ensemble; classification accuracy; latent class analysis
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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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Foody, G.; See, L.; Fritz, S.; Moorthy, I.; Perger, C.; Schill, C.; Boyd, D. Increasing the Accuracy of Crowdsourced Information on Land Cover via a Voting Procedure Weighted by Information Inferred from the Contributed Data. ISPRS Int. J. Geo-Inf. 2018, 7, 80.

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