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OpenStreetMap quality assessment using unsupervised machine learning methods / Kent T. Jacobs in Transactions in GIS, Vol 24 n° 5 (October 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 5 (October 2020) . - pp 1280-1298 Titre : OpenStreetMap quality assessment using unsupervised machine learning methods Type de document : Article/Communication Auteurs : Kent T. Jacobs, Auteur ; Scott W. Mitchell, Auteur Année de publication : 2020 Article en page(s) : pp 1280-1298 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] analyse comparative [Termes IGN] apprentissage automatique [Termes IGN] apprentissage non-dirigé [Termes IGN] approche participative [Termes IGN] Canada [Termes IGN] données localisées des bénévoles [Termes IGN] estimation de précision [Termes IGN] fiabilité des données [Termes IGN] OpenStreetMap [Termes IGN] Ottawa [Termes IGN] qualité des données Résumé : (Auteur) The reliability and quality of volunteered geographic information (VGI) continue to be pressing concerns. Many VGI projects lack standard geospatial data quality assurance procedures, and the reliability of contributors remains in question. Traditional approaches rely on comparing VGI to an “authoritative” or “gold standard” dataset to assess quality. This study investigates VGI quality by analysing the OpenStreetMap (OSM) database in Ottawa‐Gatineau, focusing on historical map features and contributor data to gain an understanding of how users are contributing to the database, and their ability to do so accurately. Unsupervised machine learning analyses expose a cluster of experienced contributors classified as “OSM validators/experts”, which are then further used to attribute data quality. They are identified through a combination of strong contribution loadings associated with the use and experience of advanced OSM editors, and weaker loadings associated with feature creation and frequency of contributions leading to further correction. Limitations are discussed with implications for future work. Numéro de notice : A2020-701 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12680 Date de publication en ligne : 18/08/2020 En ligne : https://doi.org/10.1111/tgis.12680 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96224 [article]