Underwater image analysis from A to Z: from image collection to answering key ecological questions. 

Organizers: Laurence De Clippele (University of Glasgow, United Kingdom), Carlos Dominguez Carrió (University of the Azores, Portugal), Johanne Vad (University of Edinburgh, United Kingdom)

Venue and Timing: Scottish Centre for Ecology and Natural Environment (SCENE). This is a field station which is part of the University of Glasgow (United Kingdom).

Dates: 3 -7 June 2024.

Overall goal: With the continued development of advanced but cost-effective technologies to gather high-quality underwater images, annotating and analysing vast amount of image data has become an integral part of a benthic ecologist’s role. The present workshop will provide participants the opportunity to learn about different methodologies currently available to extract biological data from underwater images, from image acquisition at sea to the exciting new AI methods to automatically annotate underwater images. The workshop will also show participants how to effectively analyse imaging data to answer different ecological questions.

Application deadline: 18:00 GMT on 22 April 2024.

How to apply:

• Applicants must be PhD students, Postdocs or Researchers (in the broadest sense, at any career stage)
• Applications should include a short CV and a motivation letter (no more than one page), stating why you want to participate in the workshop and how you will
  benefit from it. This motivation letter should also indicate (1) if you want to be considered for funding or if you will self-fund, (2) if you are willing to share a room with another ECR, (3) what your arrival and departure dates will be, (4) any dietary requirements.   
• Please email your application to MAF_Image_Analysis@outlook.com 
• The deadline for applications is 18:00 GMT on 22 April 2024. Applications received after this date will not be considered.  

Notification of acceptance: Successful applicants will be notified by the end of April.

Travel grants: 

• Applicants must be formally affiliated to or registered at a European or Associated to the EU country institution. UK applicants are also eligible.  
• Applicants must be a member of the MAF COST Action to apply for funding. Membership is free and easy – apply here to join one of the MAF working groups (https://www.cost.eu/actions/CA20102/#tabs+Name:Working%20Groups%20and%20Membership). You must do this before you can apply for travel support.  
• Successful applicants will be able to apply for reimbursement of their training school travel costs, subject to the conditions below. No advance payments will be made! 

What is covered by the funding support, and what are the rules?  

• Travel bursaries cover economy class airfare to and from the training school, up to a value of GBP 500 (~584 EUR) (strongly recommended to purchase travel insurance cancellation), plus a daily allowance of GPB 175 (~205 EUR) per day to cover hotel accommodation, local transport and meals. 
• The total amount available is capped at GBP 1,550 (1,814 EUR) per applicant.  
• Costs are reimbursed retrospectively after the training school has taken place via the e-COST portal. You must retain proof of payment for your flight ticket and boarding passes.
• You must submit your request for reimbursement via the online e-COST platform as soon as possible after the training school, but no later than Oct 20th 2024.  
• You can expect reimbursement within a few weeks of submitting your claim, subject to providing all the necessary documentation.  
• Recipients of travel bursaries through this scheme must ensure they sign the daily attendance register provided at the training school.

Session topics

• Introduction to the main tools available for underwater image acquisition; 
• Methods for manual, semi-automatic and fully automated image analysis to identify, annotate and measure benthic species from underwater images;  
• Training and applying your own Artificial Intelligence model to automate image annotation 
• Introduction to advanced statistical data analyses to explore spatial and temporal patterns in species occurrences

For more information, contact:

Please contact MAF_Image_Analysis@outlook.com