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Knowledge extraction from crowdsourced data for the enrichment of road networks / Gregor Jossé in Geoinformatica, vol 21 n° 4 (October - December 2017)  

Public [article]  inGeoinformatica > vol 21 n° 4 (October - December 2017) . - pp 763 - 795 Titre : Knowledge extraction from crowdsourced data for the enrichment of road networks Type de document : Article/Communication Auteurs : Gregor Jossé, Auteur ; Klaus Arthur Schmid, Auteur ; Andreas Züfle, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 763 - 795 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] contenu généré par les utilisateurs [Termes IGN] densification [Termes IGN] données hétérogènes [Termes IGN] données localisées des bénévoles [Termes IGN] extraction de données [Termes IGN] géopositionnement [Termes IGN] navigation [Termes IGN] production participative [Termes IGN] réseau routier [Termes IGN] utilisateur Résumé : (Auteur) In current navigation systems quantitative metrics such as distance, time and energy are used to determine optimal paths. Yet, a “best path”, as judged by users, might take qualitative features into account, for instance the scenery or the touristic attractiveness of a path. Machines are unable to quantify such “soft” properties. Crowdsourced data provides us with a means to record user choices and opinions. In this work, we survey heterogeneous sources of spatial, spatio-temporal and textual crowdsourced data as a proxy for qualitative information of users in movement. We (i) explore the process of extracting qualitative information from uncertain crowdsourced data sets employing different techniques, (ii) investigate the enrichment of road networks with the extracted information by adjusting its properties and by building a meta-network, and (iii) show how to use the enriched networks for routing purposes. An extensive experimental evaluation of our proposed methods on real-world data sets shows that qualitative properties as captured by crowdsourced data can indeed be used to improve the quality of routing suggestions while not sacrificing their quantitative aspects. Numéro de notice : A2017-603 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-017-0306-1 En ligne : https://doi.org/10.1007/s10707-017-0306-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86911 [article]