Mostafa Hoseini
Post Doctor
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Google scholarBiography
Mostafa Hoseini has started his postdoc at NIBIO since October 2022. His education background is in geomatics engineering, and his research and work experience has been mainly in the domain of global navigation satellite systems (GNSS). His tasks in the SmartForest projects revolves around developing sensor solutions to help Norwegian forest sector's digital transformation. Currently, his research in a team effort is focused on RoadSens platform for monitoring and assessment of forest roads.
Abstract
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.
Abstract
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Authors
Helle Ross Gobakken Mostafa Hoseini Stephan Hoffmann Jan Bjerketvedt Johannes Rahlf Rasmus AstrupAbstract
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