Mostafa Hoseini
Forsker
Lenker
Google scholarBiografi
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.
Forfattere
Stephan Hoffmann Mostafa Hoseini Moritz Wingartz Mahmoud Rajabi Helle Ross Gobakken Rasmus AstrupSammendrag
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
Sammendrag
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.
Sammendrag
The European Union Deforestation Regulation (EUDR) mandates traceability of timber that makes up wood products from its harvest site to the end product to ensure sustainable wood sourcing. This study proposes a cost-effective, image-based method for tracing logs using alphabetic codes printed onto logs at the harvest site. These codes are detected and interpreted through a two-stage system leveraging deep learning models. The detection stage employs YOLOv8 to locate tracking codes in images of log piles. It is trained and evaluated on a dataset of 125 images, achieving an F1-score of 0.811 on unseen images. The recognition stage, trained on 1,020 images, uses YOLOv8 models to detect individual characters and their positions within each code. On a set of unseen images, the interpretation stage is able to identify 92.8% of the individual logs despite the limited quality of the printer and degradation of the codes due to stem wetness. Analysis indicates that errors predominantly arise in the character detection step. Compared to existing traceability approaches, this method is more cost-effective than RFID tags and attains higher accuracy than image-based biomarker tracking methods.
