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Increasing the accuracy of crowdsourced information on land cover via a voting procedure weighted by information inferred from the contributed data / Giles M. Foody in ISPRS International journal of geo-information, vol 7 n° 3 (March 2018)  

Public [article]  inISPRS International journal of geo-information > vol 7 n° 3 (March 2018) Titre : Increasing the accuracy of crowdsourced information on land cover via a voting procedure weighted by information inferred from the contributed data Type de document : Article/Communication Auteurs : Giles M. Foody, Auteur ; Linda M. See, Auteur ; Steffen Fritz, Auteur ; Inian Moorthy, Auteur ; Christoph Perger, Auteur ; Christian Schill, Auteur ; Doreen S. Boyd, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] cartographie collaborative [Termes IGN] données localisées des bénévoles [Termes IGN] modèle de classe latente [Termes IGN] occupation du sol [Termes IGN] pondération [Termes IGN] précision de la classification Résumé : (Auteur) 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. Numéro de notice : A2018-093 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi7030080 Date de publication en ligne : 25/02/2018 En ligne : https://doi.org/10.3390/ijgi7030080 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89505 [article]