ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-4-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2020, 49–56, 2020
https://doi.org/10.5194/isprs-annals-V-4-2020-49-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2020, 49–56, 2020
https://doi.org/10.5194/isprs-annals-V-4-2020-49-2020

  03 Aug 2020

03 Aug 2020

EVALUATION AND OPTIMISATION OF CROWD-BASED COLLECTION OF TREES FROM 3D POINT CLOUDS

V. Walter, M. Kölle, and Y. Yin V. Walter et al.
  • Institute for Photogrammetry (ifp), Universitaet Stuttgart, Germany

Keywords: Crowd, Paid Crowdsourcing, Data Collection, 3D Point Clouds, Quality Improvement

Abstract. The term "Crowdsourcing" goes back to Jeff Howe (Howe, 2006) and represents a neologism of the words "crowd" and "outsourcing". Unlike outsourcing, where companies outsource certain tasks to known third parties, crowdsourcing outsources tasks to unknown workers (crowdworkers) on the Internet. This allows companies to access large numbers of workers who would otherwise not be available. In this paper, we will discuss an approach for the crowd-based collection of trees by means of minimum bounding cylinders from 3D point clouds. We will demonstrate the used web-interface and compare the results with reference data. To improve the quality of the results, we collect the data not only once but multiple times. This enables us to implement a so-called “Wisdom of the Crowd” approach where we can identify automatically outliers and derive integrated cylinders. We will show in this paper that this approach increases significantly the quality of the results.