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Section: Ecology
Topic: Ecology

Rapid literature mapping on the recent use of machine learning for wildlife imagery

Nakagawa, Shinichi1   ; Lagisz, Malgorzata1   ; Francis, Roxane2 ; Tam, Jessica2 ; Li, Xun3 ; Elphinstone, Andrew4; Jordan, Neil R.2, 4 ; O'Brien, Justine K.2, 4 ; Pitcher, Benjamin J.4, 5 ; Van Sluys, Monique4; Sowmya, Arcot3 ; Kingsford, Richard T.2 
1 UNSW Data Science Hub, Evolution & Ecology Research Centre and School of Biological, Earth and Environmental Sciences, UNSW, Sydney, NSW 2052, Australia
2 Centre for Ecosystem Science and School of Biological, Earth and Environmental Sciences, UNSW, Sydney, NSW 2052, Australia
3 School of Computer Science and Engineering, UNSW Sydney, NSW 2052, Australia
4 Taronga Institute of Science and Learning, Taronga Conservation Society Australia, Mosman, NSW 2088, Australia
5 School of Natural Sciences, Macquarie University, Sydney, NSW 2109, Australia

10.24072/pcjournal.261 - Peer Community Journal, Volume 3 (2023), article no. e35.

Get full text PDF Peer reviewed and recommended by PCI

Machine (especially deep) learning algorithms are changing the way wildlife imagery is processed. They dramatically speed up the time to detect, count, and classify animals and their behaviours. Yet, we currently have very few systematic literature surveys on its use in wildlife imagery. 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. We found that an increasing number of studies used convolutional neural networks (i.e., deep learning). Typically, studies have focused on large charismatic or iconic mammalian species. An increasing number of studies have been published in ecology-specific journals indicating the uptake of deep learning to transform the detection, classification and tracking of wildlife. Sharing of code was limited, with only 20% of studies providing links to analysis code. 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. Our survey, augmented with bibliometric analyses, provides valuable signposts for future studies to resolve and address shortcomings, gaps, and biases.

Published online:
DOI: 10.24072/pcjournal.261
Type: Review article
Keywords: Conservation biology, field biology, big data, research weaving, drone imagery, systematic maps, evidence synthesis, deep learning
Nakagawa, Shinichi 1; Lagisz, Malgorzata 1; Francis, Roxane 2; Tam, Jessica 2; Li, Xun 3; Elphinstone, Andrew 4; Jordan, Neil R. 2, 4; O'Brien, Justine K. 2, 4; Pitcher, Benjamin J. 4, 5; Van Sluys, Monique 4; Sowmya, Arcot 3; Kingsford, Richard T. 2

1 UNSW Data Science Hub, Evolution & Ecology Research Centre and School of Biological, Earth and Environmental Sciences, UNSW, Sydney, NSW 2052, Australia
2 Centre for Ecosystem Science and School of Biological, Earth and Environmental Sciences, UNSW, Sydney, NSW 2052, Australia
3 School of Computer Science and Engineering, UNSW Sydney, NSW 2052, Australia
4 Taronga Institute of Science and Learning, Taronga Conservation Society Australia, Mosman, NSW 2088, Australia
5 School of Natural Sciences, Macquarie University, Sydney, NSW 2109, Australia
License: CC-BY 4.0
Copyrights: The authors retain unrestricted copyrights and publishing rights 
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     author = {Nakagawa, Shinichi and Lagisz, Malgorzata and Francis, Roxane and Tam, Jessica and Li, Xun and Elphinstone, Andrew and Jordan, Neil R. and O'Brien, Justine K. and Pitcher, Benjamin J. and Van Sluys, Monique and Sowmya, Arcot and Kingsford, Richard T.},
     title = {Rapid literature mapping on the recent use of machine learning for wildlife imagery},
     journal = {Peer Community Journal},
     eid = {e35},
     publisher = {Peer Community In},
     volume = {3},
     year = {2023},
     doi = {10.24072/pcjournal.261},
     url = {https://peercommunityjournal.org/articles/10.24072/pcjournal.261/}
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Nakagawa, Shinichi; Lagisz, Malgorzata; Francis, Roxane; Tam, Jessica; Li, Xun; Elphinstone, Andrew; Jordan, Neil R.; O'Brien, Justine K.; Pitcher, Benjamin J.; Van Sluys, Monique; Sowmya, Arcot; Kingsford, Richard T. Rapid literature mapping on the recent use of machine learning for wildlife imagery. Peer Community Journal, Volume 3 (2023), article  no. e35. doi : 10.24072/pcjournal.261. https://peercommunityjournal.org/articles/10.24072/pcjournal.261/

Peer reviewed and recommended by PCI : 10.24072/pci.ecology.100513

Conflict of interest of the recommender and peer reviewers:
The recommender in charge of the evaluation of the article and the reviewers declared that they have no conflict of interest (as defined in the code of conduct of PCI) with the authors or with the content of the article.

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