Deep Learning for Applications

Deep Learning for Applications

After 2012 when deep learning based techniques won the ImageNet contest with a clear margin to competing algorithms, deep learning has been called “the revolutionary technique that quietly changed machine vision forever”. For many classification tasks deep learning has drastically surpassed previous state of the art results in classification accuracy. Currently large deep neural networks achieve the best results on speech recognition, visual object recognition, character recognition, and several language related tasks.

The power of deep learning

Deeper machine learning architectures are better capable of handling complex recognition tasks compared to previous more shallow models. Major benefits of deep networks are:

  • their superior modeling capabilities of heterogeneous data in layers of increasing complexity,
  • their ability to learn the best features to represent the raw data, and
  • their ability to gain in performance with the availability of more training data.

Specialized deep learning

The next wave of vision technology will take place for other applications than traditional computer vision, such as medical imaging, marine and seismic imaging, remote sensing of ocean, land and infrastructure, process monitoring and industry. These applications depend upon non-standard imagery and present challenges that needs to be solved in order to benefit from the untapped potential:

  • Learning from limited data sets
  • Transferring knowledge across domains
  • Exploiting non-standard and heterogeneous imagery
  • Capturing context and dependencies
  • Quantification of uncertainties in predictions
  • Reliable and explainable predictions

Combining years of experience in image analysis and machine learning

The image analysis and machine learning group at NR and the machine learning group at UiT work together to better understand the needs and to develop state-of-the-art specialized deep learning solutions suitable for solving specific problems for various industry-, medical and environmental applications.

Current activities and projects involving deep learning

Norwegian Cancer Registry

Norwegian Cancer Registry, partly funded by the Research Council of Norway
Use of deep learning and Big Data in the Norwegian Breast Cancer Screening Program


Deep learning for seismic interpretation



European Space Agency
Automatic analysis of Sentinel-data using deep learning techniques

IKTpluss, Research Council of Norway
Automatic analysis of marine data using deep learning techniques.


Orbiton AS, partly funded by the Research Council of Norway, BIA programme

Monitoring of critical infrastructure using UAVs

GE Vingmed Ultrasound, partly funded by the Research Council of Norway, BIA programme
Intelligent Cardiovascular Ultrasound Scanner


TerraTec, partly funded by the Research Council of Norway, BIA programme

Automatic mapping of forest species using deep learning

Research Council of Norway
Automatic estimation of traffic from VHR satellite images using deep learning techniques.

Infrastructure programme, Research Council of Norway
Transcription of historical Norwegian census forms

Institute of Marine Research, funded by the Research Council of Norway

Detection and counting of seals on ice from aerial images

Norwegian Mapping Authority
Tracking of small roads and forest paths from laser data

Norwegian Directorate for Cultural Heritage
Detection of cultural heritage sites from laser data

EEA grants
Automatic detection and mapping of avalanches in optical and SAR satellite images.