Modern marine ecology, particularly the study of benthic ecosystems, is increasingly driven by the acquisition of large volumes of imagery. From towed camera systems, remotely operated vehicles and autonomous underwater vehicles, underwater imaging systems now capture vast quantities of visual data. However, the interpretation of this imagery is slow, costly, and often inconsistent, creating a fundamental bottleneck that restricts the scale and speed of ecological insight.
Recent advances in computer vision methods can help to clear this bottleneck, assisting with tasks like taxonomic identification, abundance estimates, and habitat mapping. Computer vision methods have the potential to transform how benthic ecosystems are studied, enabling ecologists to work faster, more consistently, and at far greater scales. Despite this promise, many benthic ecologists do not understand the methods underpinning computer vision, lowering confidence in their application. Additionally, marine imagery presents distinct complexities for computer vision, namely the presence of turbidity, variable lighting, and cluttered scenes which cause “off-the-shelf" models to perform poorly. The lack of core computer vision skills and the unique challenges posed by underwater imagery has slowed the marine science community’s ability to adopt, adapt and harness computer vision. This workshop will address these challenges directly. It will equip benthic ecologists, primarily early-career researchers with little programming experience and no prior machine learning background, with the foundational knowledge, skills, and confidence required to integrate computer vision into their research.
All attendees will leave with reusable code, annotated Python notebooks, and complete workflows that can be easily adapted to their own datasets. The workshop will be delivered by an interdisciplinary team from the University of Cambridge, the British Antarctic Survey, and the Scottish Association for Marine Sciences.
The workshop will run over two days, hosted in-person at the British Antarctic Survey in Cambridge, 10–11th March 2026, from 10:00 - 17:00 on the 10th and 09:00 - 16:00 on the 11th. Teaching will be delivered through two complementary elements: accessible lectures introducing key concepts, and guided hands-on practicals using a real benthic dataset.
Day 1 – Foundations
- Morning lectures: Introduction to computer vision; challenges in marine imagery; effective data labelling.
- Afternoon practical: Building a benthic image dataset.
- Evening: Networking reception and dinner.
Day 2 – Applications
- Morning lectures: Model training, evaluation, and working with regulatory/industry end-users.
- Afternoon practical: Training and evaluating models using the dataset from Day 1.
By the end of this workshop participants will be able to:
- Describe the core concepts underlying modern computer vision (e.g. classification, object detection, segmentation) and how they apply to benthic imagery.
- Identify when and how computer vision is an appropriate tool for a specific research question.
- Recognise the distinct challenges of working with marine data (e.g. turbidity, colour distortion) and how they affect computer vision systems.
- Annotate benthic imagery effectively and understand how labelling types impact model performance.
- Outline best-practice workflows for computer vision, from dataset preparation and annotation to training and evaluation.
- Train and assess a computer vision model using open-source, reproducible code.
- Have the knowledge and resource to apply computer vision to their own marine image analysis problem.
Capacity: 30 participants.
Cost: Free to attend, with limited funding available to reimburse travel and accommodation for some participants.
Eligibility: Open to any UK based applicants, but all participants must be able to attend in person.
Priority:
Applications will be assessed based on their answers to the google form below, with priority given in the following order:
- PhD students and Early Career Researchers working in benthic ecology.
- Other PhD/ECR applicants with a broader interest in marine imagery.
- UKRI funding-eligible marine ecologists.
- Applicants working in other UK-based science sectors aligned with UKRI.
Lunch will be provided on both days, along with a networking dinner on 10 March. Unfortunately, remote or virtual attendance is not available.
Participants must bring:
To apply, please submit your details using this Google Form by 5pm UK time on 30/01/2026.
Dr Cameron Trotter
ML Research Scientist
British Antarctic Survey
Tom Morgan
PhD Researcher: Multimodal Machine Learning for Marine Robotics
Scottish Association for Marine Science
Dr Thomas Wilding
Benthic Ecologist
Scottish Association for Marine Science
Alejandra Mejía-Saenz
Deep-Sea Ecologist • PhD Researcher
Scottish Association for Marine Science
Dr Emily G. Mitchell
Assistant Professor
University of Cambridge
This workshop and research activities are generously funded by the Accelerate Programme for Scientific Discovery and the Cambridge Centre for Data-Driven Discovery (C2D3). Their support enables us to bridge the skills gap and provide hands-on training in computer vision for marine ecologists.
Details of all funded projects can be found in the official announcement.