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

Khan, C., Blount, D., Parham, J., Holmberg, J., Hamilton, P., Charlton, C., Christiansen, F., Johnston, D., Rayment, W., Dawson, S., Vermeulen, E., Rowntree, V., Groch, K., Levenson, J.J. and Bogucki, R. (2022) Artificial intelligence for right whale photo identification: From data science competition to worldwide collaboration. Mammalian Biology .

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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 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 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.

Item Type: Journal Article
Murdoch Affiliation(s): Centre for Sustainable Aquatic Ecosystems
Publisher: Springer Nature
Copyright: © 2022 The Authors.
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