Sediment grains

Sediment grains

Transforming ocean surveying by the power of DL and statistical methods

New project associated with Visual Intelligence on reduce bottlenecks related to uncertainty estimates in classification by a combined statistical and machine learning approach. The project is on recognition of specific objects at and within the seabed from several image types.

New associated project in Visual Intelligence: Transforming ocean surveying by the power of DL and statistical methods

The project is a collaboration between Department of Mathematics and Statistics (IMS host) and Department of Geosciences (IG) at UiT - the Arctic university of Norway.

Project Leader at IMS: Prof. Fred Godtlibsen

PIs at IG:  Reseacher  Steffen Aagaard Sørensen,  prof. Morten Hald

Funding: Research council of Norway, through the IKTPLUSS program and collaborating companies Multiconsult   and Argeo.


The amount of data collected is rapidly increasing.  Artificial intelligence is key to extract crucial information from huge amounts of data. Classifiers suggests a class affiliation based on the input data. This problem domain is denoted classification, and machine learning and statistical techniques have proven very useful. At present, these two cultures have only to a limited extent, be able to take out synergies.

Exploring the synergies are the next step to move forward. The focus of this project is to join their strengths to get rid of bottlenecks within classification in particular related to uncertainty estimation, transferable methodology and highly heterogenous data sets.

As test cases, we focus on specific tasks that are important for both the collaboration companies and for the geoscientific community. In particular, we aim to develop a product that automatize the recognition of specific objects at and within the seabed from several image acquisition types (examples in figure below) .Moreover, we will demonstrate how a time-consuming manual procedure can be replaced by an automatic system for identification and classification of objects carrying information about climatology and environmental conditions.Successful algorithms developed in the present project will pave the way for a fully automatic system, so that the human interaction of the whole process, at some point in the future, largely can be omitted.

Examples of elements at the seabed intended for targeted automated classification: A)Large scale man-object (shipwreck) detected at the seabed via high resolution synthetic aperture sonar (HISAS), (B and C) Two video still images that shows pieces of slow growing pink Coralline algae that are difficult to distinguish,(B) Image showing the nature/habitat type «maerl bed» consisting of detached spherical algae suspected of high susceptibility to ocean acidification and whose prevalence is unknown, (C) Image showing the nature/habitat main type«Grunn marin fastbunn (M1)» where common red algae form a crust on rocks/shells etc. In areas where these two nature types mix the likeness of the red algae make identification of species and establishment of nature type boundaries difficult, (D) Sample showing sediment grains, calcareous benthic foraminifera(b) and planktic foraminifera (p) as viewed through the microscope, (E)Microplastics as viewed through the microscope and zoom in on elongated microplastic fibers (m).


For more information please contact: Fred Godtliebsen

Steffen Aagaard Sørensen or Morten Hald

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