Artificial intelligence for right whale photo identification : from data science competition to worldwide collaboration

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dc.contributor.author Khan, Christin
dc.contributor.author Blount, Drew
dc.contributor.author Parham, Jason
dc.contributor.author Holmberg, Jason
dc.contributor.author Hamilton, Philip
dc.contributor.author Charlton, Claire
dc.contributor.author Christiansen, Fredrik
dc.contributor.author Johnston, David
dc.contributor.author Rayment, Will
dc.contributor.author Dawson, Steve
dc.contributor.author Vermeulen, Els
dc.contributor.author Rowntree, Victoria
dc.contributor.author Groch, Karina
dc.contributor.author Levenson, J. Jacob
dc.contributor.author Bogucki, Robert
dc.date.accessioned 2023-07-19T09:44:24Z
dc.date.available 2023-07-19T09:44:24Z
dc.date.issued 2022-06
dc.description.abstract Photo identification is an important tool in the conservation management of endangered species, and recent developments in artificial intelligence are revolutionizing existing workflows to identify individual animals. In 2015, the National Oceanic and Atmospheric Administration hosted a Kaggle data science competition to automate the identification of endangered North Atlantic right whales (Eubalaena glacialis). The winning algorithms developed by Deepsense.ai were able to identify individuals with 87% accuracy using a series of convolutional neural networks to identify the region of interest, create standardized photographs of uniform size and orientation, and then identify the correct individual. Since that time, we have brought in many more collaborators as we moved from prototype to production. Leveraging the existing infrastructure by Wild Me, the developers of Flukebook, we have created a web-based platform that allows biologists with no machine learning expertise to utilize semi-automated photo identification of right whales. New models were generated on an updated dataset using the winning Deepsense.ai algorithms. Given the morphological similarity between the North Atlantic right whale and closely related southern right whale (Eubalaena australis), we expanded the system to incorporate the largest long-term photo identification catalogs around the world including the United States, Canada, Australia, South Africa, Argentina, Brazil, and New Zealand. The system is now fully operational with multi-feature matching for both North Atlantic right whales and southern right whales from aerial photos of their heads (Deepsense), lateral photos of their heads (Pose Invariant Embeddings), flukes (CurvRank v2), and peduncle scarring (HotSpotter). We hope to encourage researchers to embrace both broad data collaborations and artificial intelligence to increase our understanding of wild populations and aid conservation efforts. en_US
dc.description.department Mammal Research Institute en_US
dc.description.librarian hj2023 en_US
dc.description.sponsorship NOAA Fisheries High Performance Computing IT Incubator, NOAA Fisheries NERAP/EBFM and the Bureau of Ocean Energy Management. Each of the photo-identification catalogs were supported by multiple funding sources over the many decades of their curation. en_US
dc.description.uri https://link.springer.com/journal/42991 en_US
dc.identifier.citation Khan, C., Blount, D., Parham, J. et al. Artificial intelligence for right whale photo identification: from data science competition to worldwide collaboration. Mammalian Biology 102, 1025–1042 (2022). https://doi.org/10.1007/s42991-022-00253-3. en_US
dc.identifier.issn 1616-5047 (print)
dc.identifier.issn 1618-1476 (online)
dc.identifier.other 10.1007/s42991-022-00253-3
dc.identifier.uri http://hdl.handle.net/2263/91533
dc.language.iso en en_US
dc.publisher Springer en_US
dc.rights © The Author(s) 2022. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License. en_US
dc.subject Automated matching en_US
dc.subject Automated pattern recognition en_US
dc.subject Computer vision en_US
dc.subject Individual identification en_US
dc.subject Machine learning en_US
dc.subject Right whale en_US
dc.subject SDG-14: Life below water en_US
dc.subject North Atlantic right whale (Eubalaena glacialis) en_US
dc.subject Southern right whale (Eubalaena australis) en_US
dc.title Artificial intelligence for right whale photo identification : from data science competition to worldwide collaboration en_US
dc.type Article en_US


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