Developing models and application to monitor the environment and climate.
Ecological studies involving e.g. classification and statistical counting of species in an ecosystem has been a challenging and time consuming task in the marine sciences. 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 (CNNs) for fish detection and classification from acoustic data are emerging.
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 are advancing deep learning in the marine sciences to overcome these challenges.
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.
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.
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.