Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2025
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RoadSens is a platform designed to expedite the digitalization process of forest roads, a cornerstone of efficient forest operations and management. We incorporate stereo-vision spatial mapping and deep-learning image segmentation to extract, measure, and analyze various geometric features of the roads. The features are precisely georeferenced by fusing post-processing results of an integrated global navigation satellite system (GNSS) module and odometric localization data obtained from the stereo camera. The first version of RoadSens, RSv1, provides measurements of longitudinal slope, horizontal/vertical radius of curvature and various cross-sectional parameters, e.g., visible road width, centerline/midpoint positions, left and right sidefall slopes, and the depth and distance of visible ditches from the road’s edges. The potential of RSv1 is demonstrated and validated through its application to two road segments in southern Norway. The results highlight a promising performance. The trained image segmentation model detects the road surface with the precision and recall values of 96.8 and 81.9 , respectively. The measurements of visible road width indicate sub-decimeter level inter-consistency and 0.38 m median accuracy. The cross-section profiles over the road surface show 0.87 correlation and 9.8 cm root mean squared error (RMSE) against ground truth. The RSv1’s georeferenced road midpoints exhibit an overall accuracy of 21.6 cm in horizontal direction. The GNSS height measurements, which are used to derive longitudinal slope and vertical curvature exhibit an average error of 5.7 cm compared to ground truth. The study also identifies and discusses the limitations and issues of RSv1, which provide useful insights into the challenges in future versions.
2024
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Abstracts from The 22nd Triennial conference of EAPR in Oslo 7-12 July 2024. The organizer of the conference was the Norwegian Institute of Bioeconomy Research (NIBIO).
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Habtamu AlemAbstract
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Ragnhild Aabøe Inglingstad Tove Gulbrandsen Devold Nicola Damiano Anna Caroline Holene Nina Svartedal Irene Comi Tone Inger Eliassen Tora Asledottir Ellen Kathrine Ulleberg Gerd VegarudAbstract
Six cattle breeds native to Norway, have for almost half a century been at risk of extinction. Due to their small population sizes, they have hardly been improved by breeding for many decades. Still, the endangered breeds represent a source of genetic diversity with special milk qualities compared to the modern breed, Norwegian red (NRF). This study reports for the first time a detailed overview of their milk composition. Milk from seven native breeds, in total 200 individuals, were included in the study. Rare genetic variants of αs1-and αs2-casein, and β-casein A1 and κ-casein B were more prevalent in milk form the endangered breeds compared to NRF. Moreover, milk from these six breeds showed better renneting properties and lower incidences of non-coagulating milk, compared to the NRF milk, which showed better acid coagulation properties. This study shows the potential for native breeds in small-scale production of high-quality rennet cheeses.
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Rune Andreassen Berit Hansen Liya Pokrovskaya Vladimir Zhakov Daniel Kling Cornelya Klutsch Ida Marie Bardalen Fløystad Hans Geir Eiken Snorre HagenAbstract
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Freya Maria Rosemarie Ziegler Vivien Rosenthal Jose G Vallarino Franziska Genzel Sarah Spettmann Łukasz Seliga Sylwia Keller-Przybyłkowicz Lucas Munnes Anita Sønsteby Sonia Osorio Björn UsadelAbstract
No abstract has been registered