RoadSens: An integrated near-field sensor solution for 3D forest road monitoring

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. • Introducing a standalone AI-powered tool to monitor forest roads using stereovision. • The road geometry characterized by several longitudinal and cross-sectional parameters. • Efficient feature extraction from 3D spatial maps via deep learning image segmentation. • GNSS and stereovision odometry are utilized to generate georeferenced point clouds. • Cross-section profiles show 9.8 cm error and 87 % correlation with ground truth.