Image analysis, machine learning and Earth observation

With more than 40 years of experience, NR has developed and adapted algorithms and methodology for image analysis, machine learning and Earth observation. We have practical experience with a wide range of applications, and detection, characterisation and object recognition are central themes in many of our projects. Our methods are used in multiple domains, including healthcare, transport, ocean, climate and environment and technology. Our success lies in simple, yet efficient solutions using methodological approaches that combine prior knowledge, context and observed data.

The department of Image analysis, machine learning and Earth observation
The department of Image analysis, machine learning and Earth observation, also called BAMJO

Image analysis

We work with various types of image data retrieved from cameras and sensors for a wide range of applications, such as healthcare, marine, and transport.

Currently, we are developing methods for automated detection of different diseases. Examples include using machine learning to detect cancer in mammograms and automating cardiac ultrasounds. Both contributions will lead to more efficient diagnostic procedures, subsequently saving time and resources for medical professionals, while maintaining a level of precision and trustworthiness that is fundamental for patient care.

In the marine sector, we use deep learning to develop methods for analysis and extraction of various marine image data, such as underwater videos and images, sonar acoustics, microscopic images and drone images of marine mammals. We work closely with our partners in the marine industry to calculate fish stocks, monitor ecosystems and ensure sustainable fisheries and harvest.

We develop algorithms for infrastructure inspection, such as recognition of faults on trainlines, by analysing images retrieved from cameras situated on trains and drones. Frequent inspections are essential to ensure safe and reliable railway systems, and automation holds great promise for cost reduction and efficiency.

Earth observation

We have been at the forefront of Earth observation in Norway since the 1980s when the country first began to focus on remote sensing via satellites. From then onwards, we have focused on developing methodologies and algorithms for analysis of remotely measured data from satellites, aircraft, and drones.

We develop methods, algorithms and tools for object recognition, classification, and parameter retrieval based on physical modelling. In the last decade, artificial intelligence, particularly deep learning, has expanded the possibilities for remote sensing across an increasing number of application areas. NR took a leading role in this domain early on, and approaches based on deep neural networks are now used in most of our applications.

Satellites have provided unprecedented opportunities for fast and repetitive mapping and monitoring of the entire world. For decades, satellites have supplied information about land, sea, atmosphere, and human activities on a daily basis. Initially, data was captured at rough resolution, but we are nearing a point where the entire globe can be mapped daily with a spatial resolution of around one metre. With airborne instruments even finer resolution can be achieved, down to a few centimetres. Drones enable us to observe specific areas and objects with millimetre-level precision.

Our vision is to conduct research and develop applications that lead to improved methods for remote sensing within environment and climate monitoring, and in mapping and monitoring natural resources and human-made objects.

To learn more about our research in image analysis, machine learning and Earth observation, please contact:

Line Eikvil

Research Director BAMJO

Rune Solberg

Research Director BAMJO

NR is part of Visual Intelligence – a Centre for Research-Based Innovation hosted by UiT The Arctic University of Norway.

Visual Intelligence logo in black

Selected research topics

Automating cardiac ultrasounds
Automatic analysis of mammograms
Deep learning for complex image data
Infrastructure inspection
Mapping and map revision
Marine image analysis
Monitoring sea and lake ice
Snow monitoring

Selected projects

The image shows a school of fish in dark waters.
  • Image analysis
  • Marine image analysis

Advancing marine services with computer vision (COGMAR)
This is a satellite image of central Norway and Sweden showing snow cover. The image colours are black, various shades of blue and purple, and of course white.
  • Earth observation

A foundation model for smarter climate action (FM4CS)
The image is split in two sections. The left side shows a standard aircraft image of a urban development area. The other section shows the same photo with hyperspectral imaging.
  • Earth observation
  • Mapping and map revision

Automated mapping (AutoKart)
The image shows a green Vy train in Norway, which has come to a halt at Sandefjord station.
  • Image analysis

Autonomous train operations with image analysis and machine learning (Europe’s Rail)
Automated analysis of drone data
  • Earth observation
  • Climate and Environment

Automated analysis of drone data (SeaBee)
The images shows a green Vy train on the railway in a Norwegian landscape. It illustrates how cameras are mounted on the front of the vehicle and what the cameras can capture (shown with different coloured triangles). The landscape is a typical Norwegian landscape with a wooden house in the background, wooded areas and snow on the ground.
  • Image analysis

Automating railway inspections (AutoKontroll)
Birch pollen against a blue sky
  • Earth observation
  • Climate and Environment

Birch pollen prediction using satellite data (Sen4Pol)
The image shows stress fibres and microtubules in human breast cancer. Image by: Christina Stuelten, Carole Parent, 2011
  • Image analysis
  • Machine learning

Breast cancer detection with machine learning (MIM)
deep learning-based methods for interpreting seismic data
  • Image analysis
  • Machine learning

Deep learning for seismic data (DELI)
Estimating vegetation height across Africa
  • Earth observation
  • Climate and Environment

Estimating vegetation height across Africa (NGVEO)
  • Image analysis
  • Machine learning

Image Analysis Railway Inspection (IARI)
  • Image analysis
  • Machine learning

Intelligent cardiac ultrasounds (INCUS)
An aerial shot of an ice sheet.
  • Earth observation
  • Climate and Environment

Machine learning and human knowledge (KnowEarth)
The images shows the burial mound at Løykja, Norway. Two large grassy mounds are covered with trees and thickets. The image is taken in the summer and the vegetation is very green.
  • Earth observation
  • Mapping and map revision

Mapping our cultural heritage (CultSearcher)
The image is a landscape shot of the bridge in Atlanterhavsvegen on the west coast of Norway. The bridge crosses a stretch of water and the land is bare and arctic. The sky is cloudy and grey.
  • Earth observation

Monitoring critical infrastructure using drones (InfraUAS)
  • Earth observation
  • Snow monitoring
  • Climate and Environment

Snow observations for climate monitoring (Snow CCI)
The images shows the body of a woman in a pink shirt holding a bright pink ribbon which symbolises breast cancer awareness.
  • Image analysis
  • Machine learning

Trustworthy AI for breast cancer screenings (AIforScreening)
Vassmyra in Sørkedalen, Oslo, between Skansebakken and Lysedammene. The back part of the peatland has scattered trees, while the surrounding area is covered by denser forest. In the middle of the peatland, there is open water.
  • Earth observation
  • Climate and Environment
  • Mapping and map revision

Using deep neural networks to map wetlands (LAVDAS)