Stephan Hoffmann
Research Scientist
Biography
My educational background encompasses International Forestry, with a B.Sc. from HNE Eberswalde and a M.Sc. from the University of Freiburg. Afterwards, my professional journey commenced in the forestry industry of Ghana, where I developed a growing passion for forest operations. This experience paved the way for me to engage in diverse applied projects worldwide, collaborating with various institutions in a range of climate zones. This journey also fueled my ambition to pursue a Ph.D. in the field of forest operations.
Consequently, I've evolved into a versatile forest operations expert, with a particular focus on steep terrain harvesting, forest road management, and the practical application of forest science.
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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