Citizen science land cover classification based on ground and aerial imagery

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

doi:10.1007/978-3-319-23374-1_14

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 proceeding › Conference contribution › peer-review

6 Scopus citations

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.

Original language	English
Title of host publication	Spatial Information Theory - 12th International Conference, COSIT 2015, Proceedings
Editors	Scott Freundshuh, Sara Irina Fabrikant, Clare Davies, Scott Bell, Michela Bertolotto, Martin Raubal
Publisher	Springer Verlag
Pages	289-305
Number of pages	17
ISBN (Print)	9783319233734
DOIs	https://doi.org/10.1007/978-3-319-23374-1_14
State	Published - 2015
Externally published	Yes
Event	12th International Conference on Spatial Information Theory, COSIT 2015 - Santa Fe, United States Duration: Oct 12 2015 → Oct 16 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9368
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Conference on Spatial Information Theory, COSIT 2015
Country/Territory	United States
City	Santa Fe
Period	10/12/15 → 10/16/15

Funding

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.

Keywords

Citizen science
Classification
Land cover

Access to Document

10.1007/978-3-319-23374-1_14

Cite this

Sparks, K., Klippel, A., Wallgrün, J. O., & Mark, D. (2015). Citizen science land cover classification based on ground and aerial imagery. In S. Freundshuh, S. I. Fabrikant, C. Davies, S. Bell, M. Bertolotto, & M. Raubal (Eds.), Spatial Information Theory - 12th International Conference, COSIT 2015, Proceedings (pp. 289-305). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9368). Springer Verlag. https://doi.org/10.1007/978-3-319-23374-1_14

Sparks, Kevin ; Klippel, Alexander ; Wallgrün, Jan Oliver et al. / Citizen science land cover classification based on ground and aerial imagery. Spatial Information Theory - 12th International Conference, COSIT 2015, Proceedings. editor / Scott Freundshuh ; Sara Irina Fabrikant ; Clare Davies ; Scott Bell ; Michela Bertolotto ; Martin Raubal. Springer Verlag, 2015. pp. 289-305 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "If citizen science is to be used in the context of environmental research, there needs to be a rigorous evaluation of humans{\textquoteright} 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{\textquoteright} 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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doi = "10.1007/978-3-319-23374-1_14",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Sparks, K, Klippel, A, Wallgrün, JO & Mark, D 2015, Citizen science land cover classification based on ground and aerial imagery. in S Freundshuh, SI Fabrikant, C Davies, S Bell, M Bertolotto & M Raubal (eds), Spatial Information Theory - 12th International Conference, COSIT 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9368, Springer Verlag, pp. 289-305, 12th International Conference on Spatial Information Theory, COSIT 2015, Santa Fe, United States, 10/12/15. https://doi.org/10.1007/978-3-319-23374-1_14

Citizen science land cover classification based on ground and aerial imagery. / Sparks, Kevin; Klippel, Alexander; Wallgrün, Jan Oliver et al.
Spatial Information Theory - 12th International Conference, COSIT 2015, Proceedings. ed. / Scott Freundshuh; Sara Irina Fabrikant; Clare Davies; Scott Bell; Michela Bertolotto; Martin Raubal. Springer Verlag, 2015. p. 289-305 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9368).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Klippel, Alexander

AU - Wallgrün, Jan Oliver

AU - Mark, David

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

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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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ER -

Sparks K, Klippel A, Wallgrün JO, Mark D. Citizen science land cover classification based on ground and aerial imagery. In Freundshuh S, Fabrikant SI, Davies C, Bell S, Bertolotto M, Raubal M, editors, Spatial Information Theory - 12th International Conference, COSIT 2015, Proceedings. Springer Verlag. 2015. p. 289-305. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-23374-1_14

Citizen science land cover classification based on ground and aerial imagery

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Keywords

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