Camera settings and biome influence the accuracy of citizen science approaches to camera trap image classification

Camera settings and biome influence the accuracy of citizen science approaches to camera trap image classification

Abstract 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...

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Bibliographic Details
Main Authors: Nicole Egna, David O'Connor, Jenna Stacy‐Dawes, Mathias W. Tobler, Nicholas Pilfold, Kristin Neilson, Brooke Simmons, Elizabeth Oneita Davis, Mark Bowler, Julian Fennessy, Jenny Anne Glikman, Lexson Larpei, Jesus Lekalgitele, Ruth Lekupanai, Johnson Lekushan, Lekuran Lemingani, Joseph Lemirgishan, Daniel Lenaipa, Jonathan Lenyakopiro, Ranis Lenalakiti Lesipiti, Masenge Lororua, Arthur Muneza, Sebastian Rabhayo, Symon Masiaine Ole Ranah, Kirstie Ruppert, Megan Owen
Format: Article
Language:English
Published: Wiley 2020-11-01
Series:Ecology and Evolution
Subjects:
amazon
crowdsource
image processing
kenya
serengeti
trail camera
Online Access:https://doi.org/10.1002/ece3.6722

Internet

https://doi.org/10.1002/ece3.6722

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