TY - GEN
T1 - Citizen science land cover classification based on ground and aerial imagery
AU - Sparks, Kevin
AU - Klippel, Alexander
AU - Wallgrün, Jan Oliver
AU - Mark, David
N1 - Funding Information:
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.
Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
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U2 - 10.1007/978-3-319-23374-1_14
DO - 10.1007/978-3-319-23374-1_14
M3 - Conference contribution
AN - SCOPUS:84951335958
SN - 9783319233734
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 289
EP - 305
BT - Spatial Information Theory - 12th International Conference, COSIT 2015, Proceedings
A2 - Freundshuh, Scott
A2 - Fabrikant, Sara Irina
A2 - Davies, Clare
A2 - Bell, Scott
A2 - Bertolotto, Michela
A2 - Raubal, Martin
PB - Springer Verlag
T2 - 12th International Conference on Spatial Information Theory, COSIT 2015
Y2 - 12 October 2015 through 16 October 2015
ER -