Analyzing Workers Performance in Online Mapping Tasks Across Web, Mobile, and Virtual Reality Platforms

Authors

  • Gerard van Alphen Delft University of Technology
  • Sihang Qiu Delft University of Technology
  • Alessandro Bozzon Delft University of Technology
  • Geert-Jan Houben Delft University of Technology

Abstract

In online crowd mapping, crowd workers recruited through crowdsourcing marketplaces collect geographic data. Compared to traditional mapping methods, where workers physically explore the area, the benefit of using online crowd mapping is the potential to be cost-effective and time-efficient. Previous studies have focused on mapping urban objects using street-level imagery. However, they are specifically aimed at a single type of object, and only through web platforms. To the best of our knowledge, there is still a lack of understanding on how workers perform the mapping tasks through different platforms. Aiming to fill this knowledge gap, we investigate the worker performance across web, mobile, and virtual reality platforms by designing a multi-platform system for mapping urban objects using street-level imagery with novel methods for geo-location estimation. We design a preliminary study to show the feasibility of executing online mapping tasks on three platforms. The result demonstrates that the type of task and execution platform can affect the worker performance in terms of worker accuracy, execution time, user engagement, and cognitive load.

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Published

2020-10-01

How to Cite

van Alphen, G., Qiu, S., Bozzon, A., & Houben, G.-J. (2020). Analyzing Workers Performance in Online Mapping Tasks Across Web, Mobile, and Virtual Reality Platforms. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 8(1), 141-149. Retrieved from https://ojs.aaai.org/index.php/HCOMP/article/view/7472