The evolution and ecology of Antarctic sea floor communities
Antarctic seafloor ecosystems are both unique and fragile. Studying their past and present gives us valuable insights into how climate change may reshape them.
Cameron Trotter
Machine Learning Research Scientist
I am a Machine Learning Research Scientist within the Artificial Intelligence (AI) Lab and the Palaeo Environments, Ice Sheets and Climate Change (PICC) team at BAS, applying computer vision to detect and classify organisms in benthic imagery as part of The past, present and future of unique cold-water benthic ecosystems in the Southern Ocean project.
I was awarded a PhD by Newcastle University for my thesis entitled ‘Towards Automatic Photo-Identification of Cetaceans: A Fine-Grained, Few-Shot Problem in Marine Ecology‘. My research focused on utilising deep computer vision to aid cetacean ecology. I developed a framework for detecting and classifying individual cetaceans in unprocessed field imagery, capable of flagging previously uncatalogued individuals through the use of latent space embeddings.
I also received my MRes in Cloud Computing for Big Data and my MComp in Computer Science from Newcastle University.
Research interests
- Computer Vision
- Fine-Grained Classification
- Few-Shot Learning
- Computational Ecology
- Marine Environments
Patton, P.T., Cheeseman, T., Abe, K., Yamaguchi, T., Reade, W., Southerland, K., Howard, A., Oleson, E.M., Allen, J.B., Ashe, E., Athayde, A., Baird, R.W., Basran, C., Cabrera, E., Calambokidis, J., Cardoso, J., Carroll, E.L., Cesario, A., Cheney, B.J., Corsi, E., Currie, J., Durban, J.W., Falcone, E.A., Fearnbach, H., Flynn, K., Franklin, T., Franklin, W., Vernazzani, B.G., Genov, T., Hill, M., Johnston, D.R., Keene, E.L., Mahaffy, S.D., McGuire, T.L., McPherson, L., Meyer, C., Michaud, R., Miliou, A., Orbach, D.N., Pearson, H.C., Rasmussen, M.H., Rayment, W.J., Rinaldi, C., Rinaldi, R., Siciliano, S., Stack, S., Tintore, B., Torres, L.G., Towers, J.R., Trotter, C., Tyson Moore, R., Weir, C.R., Wellard, W., Wells, R., Yano, K.M., Zaeschmar, J.R. and Bejder, L. (2023) ‘A deep learning approach to photo–identification demonstrates high performance on two dozen cetacean species’, in Methods in Ecology and Evolution, 00, 1-15. Available at: https://doi.org/10.1111/2041-210X.14167.
Trotter, C., Wright, N., McGough, A.S., Sharpe, M., Cheney, B., Arso Civil, M., Tyson Moore, R., Allen, J., and Berggren, P. (2022) ‘Towards Automatic Cetacean Photo-Identification: A Framework for Fine-Grain, Few-Shot Learning in Marine Ecology’, in 2022 IEEE International Conference on Big Data (Big Data). 2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan: IEEE, pp. 1942–1949. Available at: https://doi.org/10.1109/BigData55660.2022.10020942.
Curry, R., Trotter, C. and McGough, A.S. (2021) ‘Application of deep learning to camera trap data for ecologists in planning / engineering – Can captivity imagery train a model which generalises to the wild?’, in 2021 IEEE International Conference on Big Data (Big Data). 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA: IEEE, pp. 4011–4020. Available at: https://doi.org/10.1109/BigData52589.2021.9671661.
Trotter, C., Atkinson, G., Sharpe, M., Richardson, K., McGough, A.S., Wright, N., Burville, B. and Berggren, P. (2020). ‘NDD20: A large-scale few-shot dolphin dataset for coarse and fine-grained categorisation’, in The 7th Workshop on Fine-Grained Visual Categorization. 2020 CVPR Workshops. Available at: https://doi.org/10.48550/arXiv.2005.13359.
Trotter, C., Atkinson, G., Sharpe, M., McGough, A.S., Wright, N. and Berggren, P. (2019) ‘The Northumberland Dolphin Dataset: A Multimedia Individual Cetacean Dataset for Fine-Grained Categorisation’, in The 6th Workshop on Fine-Grained Visual Categorization. 2019 CVPR Workshops. Available at: https://doi.org/10.48550/arXiv.1908.02669.
Publications from NERC Open Research Archive
Trotter, C., Griffiths, H., Khan, T., Purser, A., & Whittle, R. (2025). The Weddell Sea Benthic Dataset: A computer vision-ready object detection dataset for in situ benthic biodiversity monitoring model development (Version 1.0) [Data set]. NERC EDS UK Polar Data Centre. https://doi.org/10.5285/1ba97e4b-efb7-460b-9f2d-90437e33ce09
Trotter, C., Griffiths, H., Khan, T., & Whittle, R. (2025). Automated detection of Antarctic benthic organisms in high-resolution in situ imagery to aid biodiversity monitoring: optimal model weights (Version 1.0) [Data set]. NERC EDS UK Polar Data Centre. https://doi.org/10.5285/b2874f3f-285d-4ae6-9bb4-6bfe3eacbfff
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