Transforming ocean surveying by the power of DL and statistical methods

Due to the development of increasingly more efficient computers, we are now able to utilize large amount of data in
real time. This has led to a number of everyday life applications like self-driving cars, medical decision support and
smart phones. These are all examples where Artificial Intelligence (AI) is the key to extract crucial information from
huge amounts of data.

The primary objective of this project is to improve the knowledge of AI by using combinations of statistical methods
and Deep Learning (DL). DL is a learning process where one trains an algorithm using neural networks. This is a
central part of machine learning for the development of artificial intelligence. The application in this project is image
recognition and classification of various objects from still photos, videos and acoustic images. More precise

uncertainty estimates related to image recognition and classification is an important sub-goal. We will strive to

develop solutions for (i) more reliable and accurate models; (ii) reduced uncertainty; and (iii) more interpretable

and transferable models.


The project will collaborate with the companies MultiConsult and ArGeo, that both work on monitoring and mapping
of the sea floor. We will study both large scale and smaller objects, for example lost fishery gears, seabed types,
larger plants and animals, or tiny objects such as micro-plastic particles and small animals. The project aims to
develop accurate algorithms in order to automate mapping and monitoring of the ocean floor. If successful, this will
contribute to faster and cheaper data collection, that in turn, will enhance the quality of the results and improve on
management of the ocean floor.