volume: issue, issue:
In this study, a method of automatically detecting carriageway edges and damaged areas on the surface of forest road wearing courses was tested based on high-density LiDAR data acquired using a handheld mobile laser scanning device. The results were compared with those of current tacheometric methods. Whereas most previous studies have focused on detecting road segments or objects and road centrelines using object-oriented classifications or support vector machine (SVM) algorithms, our research was directed to detect forest carriageway edges and road surface deterioration. Forest roads are designed with a 20-year lifespan before structural failures affect up to 25% of the surface area. We developed an automatic method for detecting damaged areas in the wearing course using GIS tools in ArcGIS Pro. According to the carriageway edges, an overestimation was found between the areas detected automatically and those surveyed tacheometrically, with the automatically detected area being 28% larger. However, it was also found that most of the damage detected was within the tacheometrically surveyed carriageway edges (89%). Agreement between the damage boundary overlaps was relatively low; at 57%, the total damage area detected automatically was 19% larger than that surveyed tacheometrically. The results show that the new automatic process can provide more precise, objective data, as tacheometrical methods can be influenced by the individual approach of a surveyor. Simple and quick detection of damaged areas allows assessing the condition of forest road surfaces and determining repair priorities.
volume: 47, issue: 1
In this study, a method of automatically detecting carriageway edges and damaged areas on the surface of forest road wearing courses was tested based on high-density LiDAR data acquired using a handheld mobile laser scanning device. The results were compared with those of current tacheometric methods. Whereas most previous studies have focused on detecting road segments or objects and road centrelines using object-oriented classifications or support vector machine (SVM) algorithms, our research was directed to detect forest carriageway edges and road surface deterioration. Forest roads are designed with a 20-year lifespan before structural failures affect up to 25% of the surface area. We developed an automatic method for detecting damaged areas in the wearing course using GIS tools in ArcGIS Pro. According to the carriageway edges, an overestimation was found between the areas detected automatically and those surveyed tacheometrically, with the automatically detected area being 28% larger. However, it was also found that most of the damage detected was within the tacheometrically surveyed carriageway edges (89%). Agreement between the damage boundary overlaps was relatively low; at 57%, the total damage area detected automatically was 19% larger than that surveyed tacheometrically. The results show that the new automatic process can provide more precise, objective data, as tacheometrical methods can be influenced by the individual approach of a surveyor. Simple and quick detection of damaged areas allows assessing the condition of forest road surfaces and determining repair priorities.
