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
Time and motion studies in forest operations benefit from video-based analysis, but manual annotation is time consuming. This pilot study aims to reduce analysis time by developing a deep-learning framework that classifies dashcam video into four work elements: crane out, cutting and processing, driving, and processing. Using a 3D ResNet-50 (PyTorchVideo) trained on manually annotated clips, the model achieved validation F1 = 0.88 and precision = 0.90, showing that spatiotemporal CNNs can capture rele-vant motion and appearance cues in forest environments. Overfitting indicates that more diverse data and better class balance are needed, but the approach shows clear potential to scale automated work-element monitoring and efficiency analysis.
Authors
Stephan Hoffmann Mostafa Hoseini Moritz Wingartz Mahmoud Rajabi Helle Ross Gobakken Rasmus AstrupAbstract
A functional and low-impact forest road network is essential for sustainable forest management, yet maintaining such infrastructure is costly and requires monitoring tools that are reliable and simple enough for operational use. We present an automated approach to detect, map, and evaluate forest road surface deterioration, designed to support end-users, including those with limited road expertise, to indicate required maintenance actions. The system relies on data collected by the vehicle-mounted near-field sensor platform RoadSens, which integrates stereo camera imagery with GNSS-based geo-referencing to capture detailed road surface information. Collected data are processed within a monitoring and scheduling environment using a YOLOv8 object detection model trained on nearly 14,000 annotated images. The model identifies six key deterioration features: potholes, wheel ruts, gullies, washboards, stones, and vegetation. These detections are used to locate maintenance-relevant features and classify road segments into three deterioration levels based on coverage thresholds, which are then visualized through a traffic-light system. A case study on a forest road in southern Norway demonstrated the system’s ability to detect and classify maintenance needs. While performance was strong for more uniform features such as vegetation, irregular structures like wheel ruts proved more challenging, occasionally leading to misclassification of actual maintenance requirements. Nevertheless, the findings confirm the technical feasibility of integrating object detection models into data-driven forest road maintenance scheduling. Future improvements will require larger and more diverse training datasets, as well as classification frameworks tailored to local conditions and specific road-user needs.309671 -
Authors
Janine Schweier Raffaele Spinelli Francesco Latterini Natascia Magagnotti Rodolfo Picchio Stelian A. Borz Csongor Horvath Leo G. Bont Stephan HoffmannAbstract
Purpose of review This review traces the development of mini forestry crawlers (MFCs) from earlier small-scale skidding machines to modern remote-controlled tool carriers, and evaluates their current applications, technological characteristics, operational performance, safety, soil impact, ergonomics, and automation potential. Recent findings Recent studies show that MFCs have evolved from simple extraction-oriented machines into multifunctional platforms whose suitability depends on machine class, task–machine matching, site conditions, and work organisation. Field and bench studies report productivity, soil impacts, operator workload, remote-controlled felling performance, non-harvesting applications, and early automation functions. Summary Mini forestry crawlers are most effective in constrained-access settings and in tasks that align with their limited payload while benefitting from high manoeuvrability, remote operation, and multifunctionality. Their advantages are therefore conditional on careful deployment, particularly with respect to soil moisture, turning intensity, traffic frequency, and operator workload. Future evaluations should adopt integrated performance metrics that jointly assess productivity, soil response, and human workload under realistic operating conditions.
