GILES FOODY giles.foody@nottingham.ac.uk
Professor of Geographical Information
Increasing the accuracy of crowdsourced information on land cover via a voting procedure weighted by information inferred from the contributed data
Foody, Giles; See, Linda; Fritz, Steffen; Moorthy, Inian; Perger, Christoph; Schill, Christian; Boyd, Doreen
Authors
Linda See
Steffen Fritz
Inian Moorthy
Christoph Perger
Christian Schill
DOREEN BOYD doreen.boyd@nottingham.ac.uk
Professor of Earth Observation
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.
Citation
Foody, G., See, L., Fritz, S., Moorthy, I., Perger, C., Schill, C., & Boyd, D. (2018). Increasing the accuracy of crowdsourced information on land cover via a voting procedure weighted by information inferred from the contributed data. ISPRS International Journal of Geo-Information, 7(3), https://doi.org/10.3390/ijgi7030080
Journal Article Type | Article |
---|---|
Acceptance Date | Feb 21, 2018 |
Publication Date | Feb 25, 2018 |
Deposit Date | Feb 28, 2018 |
Publicly Available Date | Feb 28, 2018 |
Journal | ISPRS International Journal of Geo-Information |
Electronic ISSN | 2220-9964 |
Publisher | MDPI |
Peer Reviewed | Peer Reviewed |
Volume | 7 |
Issue | 3 |
DOI | https://doi.org/10.3390/ijgi7030080 |
Keywords | crowdsourcing; volunteered geographic information (VGI); ensemble; classification accuracy; latent class analysis |
Public URL | https://nottingham-repository.worktribe.com/output/916793 |
Publisher URL | https://doi.org/10.3390/ijgi7030080 |
Contract Date | Feb 28, 2018 |
Files
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Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0
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