Rapid literature mapping on the recent use of machine learning for wildlife imagery Article Swipe
Shinichi Nakagawa
,
Malgorzata Lagisz
,
Roxane J. Francis
,
Jessica Tam
,
Xun Li
,
Andrew Elphinstone
,
Neil R. Jordan
,
Justine K. O’Brien
,
Benjamin J. Pitcher
,
Monique Van Sluys
,
Arcot Sowmya
,
Richard T. Kingsford
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.24072/pcjournal.261
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.24072/pcjournal.261
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.24072/pcjournal.261
- https://peercommunityjournal.org/item/10.24072/pcjournal.261.pdf
- OA Status
- diamond
- Cited By
- 8
- References
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- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4364376634
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4364376634Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.24072/pcjournal.261Digital Object Identifier
- Title
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Rapid literature mapping on the recent use of machine learning for wildlife imageryWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-04-11Full publication date if available
- Authors
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Shinichi Nakagawa, Malgorzata Lagisz, Roxane J. Francis, Jessica Tam, Xun Li, Andrew Elphinstone, Neil R. Jordan, Justine K. O’Brien, Benjamin J. Pitcher, Monique Van Sluys, Arcot Sowmya, Richard T. KingsfordList of authors in order
- Landing page
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https://doi.org/10.24072/pcjournal.261Publisher landing page
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https://peercommunityjournal.org/item/10.24072/pcjournal.261.pdfDirect link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
- OA URL
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https://peercommunityjournal.org/item/10.24072/pcjournal.261.pdfDirect OA link when available
- Concepts
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Wildlife, Artificial intelligence, Deep learning, Computer science, Machine learning, Convolutional neural network, Geography, Data science, Ecology, BiologyTop concepts (fields/topics) attached by OpenAlex
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8Total citation count in OpenAlex
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2025: 4, 2024: 1, 2023: 2, 2022: 1Per-year citation counts (last 5 years)
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76Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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