Identifying animal species in camera trap images using deep learning and citizen science-Reference-Cited by-同舟云学术

Identifying animal species in camera trap images using deep learning and citizen science

Published:2018-10-28 Issue:1 Volume:10 Page:80-91
ISSN:2041-210X
Container-title:Methods in Ecology and Evolution
language:en
Short-container-title:Methods Ecol Evol

Author:

Willi Marco¹^ORCID,Pitman Ross T.²³,Cardoso Anabelle W.⁴^ORCID,Locke Christina⁵,Swanson Alexandra⁶,Boyer Amy⁷,Veldthuis Marten⁶,Fortson Lucy¹

Affiliation:

1. School of Physics and Astronomy University of Minnesota Minneapolis Minnesota

2. Panthera New York New York

3. Department of Biological Sciences Institute for Communities and Wildlife in Africa University of Cape Town Cape Town South Africa

4. School of Geography and the Environment University of Oxford Oxford UK

5. Wisconsin Department of Natural Resources Office of Applied Science Madison Wisconsin

6. Department of Astrophysics University of Oxford Oxford UK

7. Adler Planetarium Chicago Illinois

Funder

National Science Foundation

Publisher

Wiley

Subject

Ecological Modelling,Ecology, Evolution, Behavior and Systematics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13099

Reference28 articles.

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2. Bowyer A. Maidel V. Lintott C. Swanson A. &Miller G. (2015).This image intentionally left blank: Mundane images increase citizen science participation. Retrieved fromhttps://www.humancomputation.com/2015/papers/45_Paper.pdf

3. Branson S. Horn G. V. &Perona P.(2017).Lean Crowdsourcing: Combining Humans and Machines in an Online System Cvpr 7474–7483.

4. A systematic study of the class imbalance problem in convolutional neural networks

5. Citizen Science as an Ecological Research Tool: Challenges and Benefits

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