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International Conference on Spatial Information Theory

COSIT 2015: Spatial Information Theory pp 289–305Cite as

Citizen Science Land Cover Classification Based on Ground and Aerial Imagery

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 9368)

Abstract

If citizen science is to be used in the context of environmental research, there needs to be a rigorous evaluation of humans’ cognitive ability to interpret and classify environmental features. This research, with a focus on land cover, explores the extent to which citizen science can be used to sense and measure the environment and contribute to the creation and validation of environmental data. We examine methodological differences and humans’ ability to classify land cover given different information sources: a ground-based photo of a landscape versus a ground and aerial based photo of the same location. Participants are solicited from the online crowdsourcing platform Amazon Mechanical Turk. Results suggest that across methods and in both ground-based, and ground and aerial based experiments, there are similar patterns of agreement and disagreement among participants across land cover classes. Understanding these patterns is critical to form a solid basis for using humans as sensors in earth observation.

Keywords

  • Land cover
  • Citizen science
  • Classification

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Acknowledgments

This research is funded by the National Science Foundation (#0924534). We would like to thank the Degree Confluence Project for permission to use photos from the confluence.org website for our research. We would like to thank the members of the Human Factors in GIScience laboratory.

Author information

Authors and Affiliations

  1. The Pennsylvania State University, University Park, State College, PA, 16801, USA

    Kevin Sparks, Alexander Klippel & Jan Oliver Wallgrün

  2. NCGIA and Department of Geography, University at Buffalo, Buffalo, NY, 14228, USA

    David Mark

Authors
  1. Kevin Sparks
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  2. Alexander Klippel
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  3. Jan Oliver Wallgrün
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  4. David Mark
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Corresponding author

Correspondence to Kevin Sparks .

Editor information

Editors and Affiliations

  1. University of Zurich, Zurich, Switzerland

    Sara Irina Fabrikant

  2. ETH Zurich, Zurich, Switzerland

    Martin Raubal

  3. School of Computer Science and Informatics, University College Dublin, Dublin, Ireland

    Michela Bertolotto

  4. University of Winchester, Winchester, United Kingdom

    Clare Davies

  5. Dept. of Geography and Env. Studies, University of New Mexico, Albuquerque, New Mexico, USA

    Scott Freundschuh

  6. College of Arts and Science, University of Saskatchewan, Saskatoon, Canada

    Scott Bell

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Sparks, K., Klippel, A., Wallgrün, J.O., Mark, D. (2015). Citizen Science Land Cover Classification Based on Ground and Aerial Imagery. In: Fabrikant, S., Raubal, M., Bertolotto, M., Davies, C., Freundschuh, S., Bell, S. (eds) Spatial Information Theory. COSIT 2015. Lecture Notes in Computer Science(), vol 9368. Springer, Cham. https://doi.org/10.1007/978-3-319-23374-1_14

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  • DOI: https://doi.org/10.1007/978-3-319-23374-1_14

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