Rapid literature mapping on the recent use of machine learning for wildlife imagery
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 l...
Saved in:
Main Authors: | 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. |
---|---|
Format: | Article |
Language: | English |
Published: |
Peer Community In
2023-04-01
|
Series: | Peer Community Journal |
Online Access: | https://peercommunityjournal.org/articles/10.24072/pcjournal.261/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Poor hypotheses and research waste in biology: learning from a theory crisis in psychology
by: Shinichi Nakagawa, et al.
Published: (2025-02-01) -
A framework for assessing reliability of observer annotations of aerial wildlife imagery, with insights for deep learning applications
by: Rowan L. Converse, et al.
Published: (2025-01-01) -
Insecticides and Wildlife
by: John L. Capinera
Published: (2012-02-01) -
Insecticides and Wildlife
by: John L. Capinera
Published: (2012-02-01) -
Imagery Ability and Imagery Perspective Preference: A Study of Their Relationship and Age- and Gender-Related Changes
by: Karen P. Y. Liu, et al.
Published: (2019-01-01)