Marine sciences

Developing models and applications to monitor the marine environment

Background

Ecological studies, involving e.g. classification and statistical counting of species in an ecosystem, has been challenging and time consuming tasks in the marine sciences. Efficient and reliable data-driven methods for automatic analysis of complex marine observation data are needed to ensure sustainable fisheries and harvest. Deep learning has the potential to automate and streamline the steps required in such studies, but few applications in this domain has been developed.

An important source of data in marine sciences is the echo sounder which is used to observe the marine ecosystem at a larger scale. Some preliminary approaches using convolutional neural networks for fish detection and classification from acoustic data are emerging but very little research has been done in this area.

Main objective

The innovations in the field of marine sciences aim developing  efficient and reliable deep learning methods for automatic analysis of complex marine observation data.

Challenges

Many of the challenges related to the use of deep learning in this area are related to training data, where the amount of annotations can be limited and the quality variable and it is too expensive to get more and/or better data. There is also a need for explainability and reliability, as trust becomes very important when the output from these systems are intended as input to models for abundance estimation which again is a basis for setting of fishing quotas.

Visual Intelligence is advancing deep learning in the marine sciences to overcome these challenges.

Related news

New paper on Machine Learning + Marine Science and the critical partnerships in Norway
October 15, 2021

The paper review some recent advances in developing machine learning methods for marine science applications in Norway. It is published in the Journal of Ocean Technology 2021.

Stream our latest seminars
May 27, 2021

Did you miss any of our recent seminars? When we host seminars and events we often record relevant talks and presentations and make them available at our youtube channel. You can access all our content through our "outreach" page.

Annual report 2020
May 20, 2021

SFI Visual Intelligence has published the annual report for 2020. The report is approved by the Visual Intelligence board and available for download as a pdf document under "publicity".

Visual Intelligence Graduate School (VIGS)
May 20, 2021

SFI Visual Intelligence is organizing a graduate school for early career research fellows connected to Visual Intelligence. VIGS aims at connecting research fellows across our different research institutions to build social and professional networks.

Northern Lights Deep Learning Workshop 2021
January 19, 2021

NLDL 2021 will be a digital conference hosted by the UiT Machine Learning Group and Visual Intelligence January 18-20. The program includes a Mini Deep Learning School the 18th and is followed by a tight program the rest of the week.

A new Centre for Research-based Innovation
January 19, 2021

Visual Intelligence will be one of the new SFIs funded by the Research Council of Norway. The center will run over a period of eight years and will form a collaboration between businesses and research institutions in Norway.

Visual Intelligence is officially opened!
January 19, 2021

The official opening of SFI Visual Intelligence was successfully arranged as a digital event today. We are now ready to commence our research and innovation to tackle some of the large challenges in deep learning and AI, along with our partners.

Related projects

Detection and classification of fish species from acoustic data
December 15, 2020
We collaborate with the Institute of Marine Research (IMR) to develop models and applications to detect and classify fish from echosounders.
Detection of sea mammals from aerial imagery
December 14, 2020
Better solutions are needed to estimate the populations of sea mammals, such as breeding seals, from aerial images of the sea ice.