Helle Ross Gobakken
PhD Candidate
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
Abstract
Collection, processing and provision of comprehensive geometric information of forest roads is decisive for its technical classification to facilitate sustainable timber supply chains. An automized classification system based on the mobile proximal sensor platform RoadSens was developed, applied and validated through a case study approach in Eastern Norway. Six sample roads of various vegetation stages were surveyed through RoadSens and complemented through sampled total station measurements for validation purposes. The determined geometric parameters road slope, curvature and width were used for technical classification following the national forest road standard. Road width was identified as the main constraint in meeting the standard, resulting in a general downgrading of the sampled roads according to its technical class. The results showed a root mean square error (RMSE) ranging from ±0.53 to 1.50 m (12–33%) depending on the road and vegetation stage compared to the validation data. Despite these accuracy constraints, the application case study already indicates a general need for improvement of road data acquisition and updating of associated databases. The study underscores that, despite the challenges and limitations, there is a clear need for an automated sensing and classification system, which offers a cost-effective alternative to manual surveying and requires less specialized expertise.
Abstract
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