Speaker
Description
Reliable and up-to-date forest road information is essential for sustainable forest operations, affecting technical road classification, timber transport accessibility, and maintenance planning. However, conventional road inventories are often incomplete, outdated, and costly to maintain due to their reliance on manual surveys and expert assessments. Over the past four years, we have developed RoadSens, a mobile proximal sensing and analytics platform designed as a technical solution for comprehensive forest road digitalization, enabling multiple operational applications from a single data acquisition workflow. This contribution synthesizes the development, methodological advances, and validation of the RoadSens platform.
RoadSens as a flexible to use vehicle attachment, integrating stereo vision spatial mapping, deep learning–based perception, and precise geo referencing through the fusion of post processed GNSS and stereo derived odometry. The platform provides a detailed geometric description of forest roads, including longitudinal slope, horizontal and vertical curvature, and cross sectional parameters such as road width, centerline position, sidefall slopes, and ditch characteristics. Validation across several forest roads in Norway demonstrates that the platform is capable of delivering consistent, spatially continuous geometric information suitable for operational use.
This geometric foundation enables multiple downstream applications. First, RoadSens supports automated technical road classification according to defined road standards, using geometry based criteria such as slope, curvature, and width. Case study results indicate that road width is often the dominant constraint in meeting technical standards, underlining the value of systematic and up to date geometric data. Second, RoadSens provides essential input for operational planning, including the assessment of accessibility and restrictions for timber trucks, by delivering road parameters relevant for vehicle performance and safety. Third, the platform has been extended towards maintenance planning through automated detection and mapping of surface deterioration features, which are aggregated into intuitive, segment level maintenance indicators.
Across all applications, the main current bottleneck is not the sensing or geo referencing concept, but the performance of object detection models used for road surface interpretation. While technically feasible, detection of irregular and heterogeneous deterioration features remains challenging. Importantly, this limitation is primarily data driven and can be systematically addressed through larger, more diverse, and higher quality training datasets, making the approach well suited for continuous improvement as additional data become available.
Overall, RoadSens demonstrates the potential of mobile sensing as a scalable and cost effective alternative to manual forest road surveys, providing a unified data basis for technical classification, operational planning, and maintenance decision support.
| Keywords | proximal-sensing; road-geometry; deep-learning; maintenance; |
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