Rapid literature mapping on the recent use of machine learning for wildlife imageryuse asterix (*) to get italics

Shinichi Nakagawa, Malgorzata Lagisz, Roxane Francis, Jessica Tam, Xun Li, Andrew Elphinstone, Neil R. Jordan, Justine K. O’Brien, Benjamin J. Pitcher, Monique Van Sluys, Arcot Sowmya, Richard T. KingsfordPlease use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"

2023

<p>1. Machine (especially deep) learning algorithms are changing the way wildlife imagery is processed. They dramatically speed up the time to detect, count, classify animals and their behaviours. Yet, we currently have a very few systematic literature surveys on its use in wildlife imagery.</p> <p>2. Through a literature survey (a ‘rapid’ review) and bibliometric mapping, we explored its use across: 1) species (vertebrates), 2) image types (e.g., camera traps, or drones), 3) study locations, 4) alternative machine learning algorithms, 5) outcomes (e.g., recognition, classification, or tracking), 6) reporting quality and openness, 7) author affiliation, and 8) publication journal types.&nbsp;</p> <p>3. We found that increasing number of studies used convolutional neural networks (i.e., deep learning). Typically, studies have focused on large charismatic or iconic mammalian species . Increasing number of studies is published in ecology-specific journals indicating the uptake of deep learning to transform detection, classification and tracking of wildlife. Sharing of code was limited, with only 20% of studies providing links to analysis code.&nbsp;</p> <p>4. Much of the published research and focus on animals came from India, China, Australia, or the USA. There were relatively few collaborations across countries. Given the power of machine learning, we recommend increasing collaboration and sharing approaches to utilise increasing amounts of wildlife imagery more rapidly and transform and improve understanding of wildlife behaviour and conservation.</p> <p>5. Our survey augmented with bibliometric analyses provide valuable signposts for future studies to resolve and address shortcomings, gaps, and biases.&nbsp;</p> <p>&nbsp;</p> <p>&nbsp;</p>

https://doi.org/10.5281/zenodo.7502948You should fill this box only if you chose 'All or part of the results presented in this preprint are based on data'. URL must start with http:// or https://

https://doi.org/10.5281/zenodo.7502948You should fill this box only if you chose 'Scripts were used to obtain or analyze the results'. URL must start with http:// or https://

https://doi.org/10.5281/zenodo.7502948You should fill this box only if you chose 'Codes have been used in this study'. URL must start with http:// or https://

Conservation biology, field biology, big data, research weaving, drone imagery, systematic maps, evidence synthesis, deep learning

NonePlease indicate the methods that may require specialised expertise during the peer review process (use a comma to separate various required expertises).

Behaviour & Ethology, Conservation biology

2022-10-31 22:05:46

Olivier Gimenez

Anonymous