SAMBA

Statistical Analysis, Machine Learning and Image Analysis - SAMBA

The SAMBA department has comprehensive theoretical and practical knowledge in the fields of statistics, machine learning and image analysis. We are one of Europe's largest and most competent groups within applied statistics and statistical-matematical modelling. We cover a broad spectrum of methods and are a world leader in some of these areas. The appropriate choice of method for the various problems is thus one of our strengths. Many calculations involve uncertainty and the accurate calculation of this quantity is an important speciality.

Research areas

Statistics for climate and environment

Our research within statistics for climate and environment is a mixture of applied, problem-driven projects and fundamental research eligible for publication in scientific journals.

Statistics for finance, insurance and commodity markets

We use statistical methods to quantify all kinds of risk in the banking, insurance and commodity markets. We work a lot with models related to Basel II and Solvency II and have developed a large number of systems related to trading and risk management for various power companies.

Statistics for marine resources and health

We cover a wide range of applications within the areas marine resources and health.

Statistics for technology, industry and administration

We cover a wide range of applications including forecasting, time series analysis, network modelling, optimal delivery, logistics, optimal steering of industry processes, reliability analysis, cost surveys and analysis of very large data sets.

Earth observation

We do research and development of methodologies for analysis of digital remote sensing data from satellites and aircrafts. Application areas include environmental monitoring, climate change monitoring, environmental emergency detection, natural resource mapping and map revision.

Image Analysis and Machine Learning

We have extensive knowledge in the fields of image analysis and machine learning. Object extraction, characterization and recognition from images are central for many of these applications. Another central theme is the analysis of sequences of data, including motion detection and characterization.

Big Insight

This center for research-based-innovation is funded by the Research Council of Norway. Big Insight produces innovative solutions for key challenges facing our consortium of private, public and research partners, by developing original statistical and machine learning methodologies.

Last 5 scientific articles

Hubin, Aliaksandr; Storvik, Geir Olve; Frommlet, Florian. A Novel Algorithmic Approach to Bayesian Logic Regression. Bayesian Analysis (ISSN 1936-0975). doi: 10.1214/18-BA1141. 2018.

Liu, Qinghui; Salberg, Arnt Børre; Jenssen, Robert. A Comparison of Deep Learning Architectures for Semantic Mapping of Very High Resolution Images. In: IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. (ISBN 978-1-5386-7150-4). pp 6943-6946. doi: 10.1109/IGARSS.2018.8518533. 2018. Full-text

Kermit, Martin Andreas; Hamar, Jarle Bauck; Trier, Øivind Due. Towards a national infrastructure for semi-automatic mapping of cultural heritage in Norway. In: Oceans of data. Proceedings of the 44th Annual Conference on Computer Applications and Quantitative Methods in Archaeology. (ISBN 9781784917302). pp 159-172. 2018. Full-text

Salberg, Arnt Børre; Larsen, Siri Øyen. Classification of Ocean Surface Slicks in Simulated Hybrid-Polarimetric SAR Data. IEEE Transactions on Geoscience and Remote Sensing (ISSN 0196-2892). 56(12) pp 7062-7073. doi: 10.1109/TGRS.2018.2847724. 2018. Full-text

Trier, Øivind Due; Cowley, David C.; Waldeland, Anders U.. Using deep neural networks on airborne laser scanning data: results from a case study of semi-automatic mapping of archaeological topography on Arran, Scotland. Archaeological Prospection (ISSN 1075-2196). 26(1) doi: 10.1002/arp.1731. 2018. Full-text

Publications in 2019, 2018, 2017, 2016, 2015, earlier years