Skip to main content
Journal cover image

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

Publication ,  Journal Article
Khan, C; Blount, D; Parham, J; Holmberg, J; Hamilton, P; Charlton, C; Christiansen, F; Johnston, D; Rayment, W; Dawson, S; Vermeulen, E ...
Published in: Mammalian Biology
June 1, 2022

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.

Duke Scholars

Altmetric Attention Stats
Dimensions Citation Stats

Published In

Mammalian Biology

DOI

EISSN

1618-1476

ISSN

1616-5047

Publication Date

June 1, 2022

Volume

102

Issue

3

Start / End Page

1025 / 1042

Related Subject Headings

  • Ecology
  • 3109 Zoology
  • 3104 Evolutionary biology
  • 0608 Zoology
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Khan, C., Blount, D., Parham, J., Holmberg, J., Hamilton, P., Charlton, C., … Bogucki, R. (2022). Artificial intelligence for right whale photo identification: from data science competition to worldwide collaboration. Mammalian Biology, 102(3), 1025–1042. https://doi.org/10.1007/s42991-022-00253-3
Khan, C., D. Blount, J. Parham, J. Holmberg, P. Hamilton, C. Charlton, F. Christiansen, et al. “Artificial intelligence for right whale photo identification: from data science competition to worldwide collaboration.” Mammalian Biology 102, no. 3 (June 1, 2022): 1025–42. https://doi.org/10.1007/s42991-022-00253-3.
Khan C, Blount D, Parham J, Holmberg J, Hamilton P, Charlton C, et al. Artificial intelligence for right whale photo identification: from data science competition to worldwide collaboration. Mammalian Biology. 2022 Jun 1;102(3):1025–42.
Khan, C., et al. “Artificial intelligence for right whale photo identification: from data science competition to worldwide collaboration.” Mammalian Biology, vol. 102, no. 3, June 2022, pp. 1025–42. Scopus, doi:10.1007/s42991-022-00253-3.
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 JJ, Bogucki R. Artificial intelligence for right whale photo identification: from data science competition to worldwide collaboration. Mammalian Biology. 2022 Jun 1;102(3):1025–1042.
Journal cover image

Published In

Mammalian Biology

DOI

EISSN

1618-1476

ISSN

1616-5047

Publication Date

June 1, 2022

Volume

102

Issue

3

Start / End Page

1025 / 1042

Related Subject Headings

  • Ecology
  • 3109 Zoology
  • 3104 Evolutionary biology
  • 0608 Zoology