Citizen science land cover classification based on ground and aerial imagery

Kevin Sparks, Alexander Klippel, Jan Oliver Wallgrün, David Mark

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationSpatial Information Theory - 12th International Conference, COSIT 2015, Proceedings
EditorsScott Freundshuh, Sara Irina Fabrikant, Clare Davies, Scott Bell, Michela Bertolotto, Martin Raubal
PublisherSpringer Verlag
Number of pages17
ISBN (Print)9783319233734
StatePublished - 2015
Event12th International Conference on Spatial Information Theory, COSIT 2015 - Santa Fe, United States
Duration: Oct 12 2015Oct 16 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other12th International Conference on Spatial Information Theory, COSIT 2015
Country/TerritoryUnited States
CitySanta Fe

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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