Section: Ecology
Topic:
Ecology
Rapid literature mapping on the recent use of machine learning for wildlife imagery
Corresponding author(s): Nakagawa, Shinichi (s.nakagawa@unsw.edu.au); Lagisz, Malgorzata (m.lagisz@unsw.edu.au)
10.24072/pcjournal.261 - Peer Community Journal, Volume 3 (2023), article no. e35.
Get full text PDF Peer reviewed and recommended by PCIMachine (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.
Type: Review article
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
@article{10_24072_pcjournal_261, 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/} }
TY - JOUR AU - Nakagawa, Shinichi AU - Lagisz, Malgorzata AU - Francis, Roxane AU - Tam, Jessica AU - Li, Xun AU - Elphinstone, Andrew AU - Jordan, Neil R. AU - O'Brien, Justine K. AU - Pitcher, Benjamin J. AU - Van Sluys, Monique AU - Sowmya, Arcot AU - Kingsford, Richard T. TI - Rapid literature mapping on the recent use of machine learning for wildlife imagery JO - Peer Community Journal PY - 2023 VL - 3 PB - Peer Community In UR - https://peercommunityjournal.org/articles/10.24072/pcjournal.261/ DO - 10.24072/pcjournal.261 ID - 10_24072_pcjournal_261 ER -
%0 Journal Article %A Nakagawa, Shinichi %A Lagisz, Malgorzata %A Francis, Roxane %A Tam, Jessica %A Li, Xun %A Elphinstone, Andrew %A Jordan, Neil R. %A O'Brien, Justine K. %A Pitcher, Benjamin J. %A Van Sluys, Monique %A Sowmya, Arcot %A Kingsford, Richard T. %T Rapid literature mapping on the recent use of machine learning for wildlife imagery %J Peer Community Journal %D 2023 %V 3 %I Peer Community In %U https://peercommunityjournal.org/articles/10.24072/pcjournal.261/ %R 10.24072/pcjournal.261 %F 10_24072_pcjournal_261
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/
PCI peer reviews and recommendation, and links to data, scripts, code and supplementary information: 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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