Camera settings and biome influence the accuracy of citizen science approaches to camera trap image classification
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Issue Date
2020Author
Egna, NicoleO'Connor, David
Stacy-Dawes, Jenna
Tobler, Mathias W.
Pilfold, Nicholas W.
Neilson, Kristin
Simmons, Brooke
Davis, Elizabeth Oneita
Bowler, Mark
Fennessy, Julian
Glikman, Jenny A.
Larpei, Lexson
Lekalgitele, Jesus
Lekupanai, Ruth
Lekushan, Johnson
Lemingani, Lekuran
Lemirgishan, Joseph
Lenaipa, Daniel
Lenyakopiro, Jonathan
Lesipiti, Ranis Lenalakiti
Lororua, Masenge
Muneza, Arthur
Rabhayo, Sebastian
Ranah, Symon Masiaine Ole
Ruppert, Kirstie
Owen, Megan A.
Journal title
Ecology and EvolutionVolume
10Issue
21Begin page
11954End page
11965
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https://onlinelibrary.wiley.com/doi/abs/10.1002/ece3.6722Abstract
Scientists are increasingly using volunteer efforts of citizen scientists to classify images captured by motion-activated trail cameras. The rising popularity of citizen science reflects its potential to engage the public in conservation science and accelerate processing of the large volume of images generated by trail cameras. While image classification accuracy by citizen scientists can vary across species, the influence of other factors on accuracy is poorly understood. Inaccuracy diminishes the value of citizen science derived data and prompts the need for specific best-practice protocols to decrease error. We compare the accuracy between three programs that use crowdsourced citizen scientists to process images online: Snapshot Serengeti, Wildwatch Kenya, and AmazonCam Tambopata. We hypothesized that habitat type and camera settings would influence accuracy. To evaluate these factors, each photograph was circulated to multiple volunteers. All volunteer classifications were aggregated to a single best answer for each photograph using a plurality algorithm. Subsequently, a subset of these images underwent expert review and were compared to the citizen scientist results. Classification errors were categorized by the nature of the error (e.g., false species or false empty), and reason for the false classification (e.g., misidentification). Our results show that Snapshot Serengeti had the highest accuracy (97.9%), followed by AmazonCam Tambopata (93.5%), then Wildwatch Kenya (83.4%). Error type was influenced by habitat, with false empty images more prevalent in open-grassy habitat (27%) compared to woodlands (10%). For medium to large animal surveys across all habitat types, our results suggest that to significantly improve accuracy in crowdsourced projects, researchers should use a trail camera set up protocol with a burst of three consecutive photographs, a short field of view, and determine camera sensitivity settings based on in situ testing. Accuracy level comparisons such as this study can improve reliability of future citizen science projects, and subsequently encourage the increased use of such data.Type
ArticleRights
© 2020 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/Rights link
https://creativecommons.org/licenses/by/4.0/ae974a485f413a2113503eed53cd6c53
10.1002/ece3.6722
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Except where otherwise noted, this item's license is described as © 2020 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/