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Verfasst von:Herfort, Benjamin [VerfasserIn]   i
 Höfle, Bernhard [VerfasserIn]   i
 Klonner, Carolin [VerfasserIn]   i
Titel:3D micro-mapping
Titelzusatz:towards assessing the quality of crowdsourcing to support 3D point cloud analysis
Verf.angabe:Benjamin Herfort, Bernhard Höfle, Carolin Klonner
E-Jahr:2018
Jahr:17 February 2018
Umfang:11 S.
Fussnoten:Available online 17 February 2018 ; Gesehen am 18.12.2018
Titel Quelle:Enthalten in: International Society for Photogrammetry and Remote SensingISPRS journal of photogrammetry and remote sensing
Ort Quelle:Amsterdam [u.a.] : Elsevier, 1989
Jahr Quelle:2018
Band/Heft Quelle:137(2018), Seite 73-83
ISSN Quelle:0924-2716
Abstract: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.
DOI:doi:10.1016/j.isprsjprs.2018.01.009
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext ; Verlag: https://doi.org/10.1016/j.isprsjprs.2018.01.009
 Volltext: http://www.sciencedirect.com/science/article/pii/S0924271618300091
 DOI: https://doi.org/10.1016/j.isprsjprs.2018.01.009
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Crowdsourcing
 LiDAR
 Point cloud classification
 Quality
 Urban trees
K10plus-PPN:1685937446
Verknüpfungen:→ Zeitschrift

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