Centre de documentation
scientifique

3D micro-mapping : Towards assessing the quality of crowdsourcing to support 3D point cloud analysis / Benjamin Herfort in ISPRS Journal of photogrammetry and remote sensing, vol 137 (March 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 137 (March 2018) . - pp 73 - 83 Titre : 3D micro-mapping : Towards assessing the quality of crowdsourcing to support 3D point cloud analysis Type de document : Article/Communication Auteurs : Benjamin Herfort, Auteur ; Bernhard Höfle, Auteur ; Carolin Klonner, Auteur Année de publication : 2018 Article en page(s) : pp 73 - 83 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] arbre (flore) [Termes IGN] cartographie collaborative [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] données localisées des bénévoles [Termes IGN] évaluation des données [Termes IGN] production participative [Termes IGN] qualité des données [Termes IGN] semis de points [Termes IGN] ville Résumé : (Auteur) In this paper, we propose a method to crowdsource the task of complex three-dimensional information extraction from 3D point clouds. We design web-based 3D micro tasks tailored to assess segmented LiDAR point clouds of urban trees and investigate the quality of the approach in an empirical user study. Our results for three different experiments with increasing complexity indicate that a single crowdsourcing task can be solved in a very short time of less than five seconds on average. Furthermore, the results of our empirical case study reveal that the accuracy, sensitivity and precision of 3D crowdsourcing are high for most information extraction problems. For our first experiment (binary classification with single answer) we obtain an accuracy of 91%, a sensitivity of 95% and a precision of 92%. For the more complex tasks of the second Experiment 2 (multiple answer classification) the accuracy ranges from 65% to 99% depending on the label class. Regarding the third experiment – the determination of the crown base height of individual trees – our study highlights that crowdsourcing can be a tool to obtain values with even higher accuracy in comparison to an automated computer-based approach. Finally, we found out that the accuracy of the crowdsourced results for all experiments is hardly influenced by characteristics of the input point cloud data and of the users. Importantly, the results’ accuracy can be estimated using agreement among volunteers as an intrinsic indicator, which makes a broad application of 3D micro-mapping very promising. Numéro de notice : A2018-078 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.01.009 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.01.009 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89440 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018031	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2018033	DEP-EXM	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2018032	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt