Stefano Puliti
Forsker
Forfattere
Stefano Puliti Emily R. Lines Jana Müllerová Julian Frey Zoe Schindler Adrian Straker Matthew J. Allen Lukas Winiwarter Nataliia Rehush Hristina Hristova Brent Murray Kim Calders Nicholas Coops Bernhard Höfle Liam Irwin Samuli Junttila Martin Krůček Grzegorz Krok Kamil Král Shaun R. Levick Linda Luck Azim Missarov Martin Mokroš Harry J. F. Owen Krzysztof Stereńczak Timo P. Pitkänen Nicola Puletti Ninni Saarinen Chris Hopkinson Louise Terryn Chiara Torresan Enrico Tomelleri Hannah Weiser Rasmus AstrupSammendrag
Det er ikke registrert sammendrag
Sammendrag
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.
Sammendrag
Det er ikke registrert sammendrag
Divisjon for skog og utmark
PathFinder
Towards an Integrated Consistent European LULUCF Monitoring and Policy Pathway Assessment Framework
Divisjon for skog og utmark
Automating Biodiversity Mapping: AI Applications in the Identification of Forest Habitats
I dette prosjektet skal vi undersøke hvordan KI og smarttelefoner kan brukes ved kartlegging av viktige skogstrukturer, og komme forbi begrensninger med tradisjonelt feltarbeid.
Divisjon for skog og utmark
Lukkede hogster: Konsekvenser for produksjon, økonomi og biomangfold
Hovedmålet til prosjektet er å bygge kunnskap og kompetanse om hvordan en omlegging av norsk skogbruk, fra flatehogst til mer bruk av lukkede hogster, vil påvirke produksjon, økonomi og biologisk mangfold.
Divisjon for skog og utmark
SFI SmartForest: Bringing Industry 4.0 to the Norwegian forest sector
SmartForest will position the Norwegian forest sector at the forefront of digitalization resulting in large efficiency gains in the forest sector, increased production, reduced environmental impacts, and significant climate benefits. SmartForest will result in a series of innovations and be the catalyst for an internationally competitive forest-tech sector in Norway. The fundamental components for achieving this are in place; a unified and committed forest sector, a leading R&D environment, and a series of progressive data and technology companies.