Detecting Terrain Stoniness From Airborne Laser Scanning Data †

Abstract

Three methods to estimate the presence of ground surface stones from publicly available Airborne Laser Scanning (ALS) point clouds are presented. The first method approximates the local curvature by local linear multi-scale fitting, and the second method uses Discrete-Differential Gaussian curvature based on the ground surface triangulation. The third baseline method applies Laplace filtering to Digital Elevation Model (DEM) in a 2 m regular grid data. All methods produce an approximate Gaussian curvature distribution which is then vectorized and classified by logistic regression. Two training data sets consisted of 88 and 674 polygons of mass-flow deposits, respectively. The locality of the polygon samples is a sparse canopy boreal forest, where the density of ALS ground returns is sufficiently high to reveal information about terrain micro-topography. The surface stoniness of each polygon sample was categorized for supervised learning by expert observation on the site. The leave-pair-out (L2O) cross-validation of the local linear fit method results in the area under curve AUC = 0.74 and AUC = 0.85 on two data sets, respectively. This performance can be expected to suit real world applications such as detecting coarse-grained sediments for infrastructure construction. A wall-to-wall predictor based on the study was demonstrated.

Estimating the Rut Depth by UAV Photogrammetry

“…It seems to be mathematically possible to utilize the local orientation information of [37], but at the moment, the noise level of the orientation is too high. The noise signal of the small trees, which do not completely get eliminated by solid angle filtering (SAF) [25], is particularly problematic. Multiple classification to ruts/young trees/open ground/canopy could be a solution.…”

“…Ground height model: We produced TIN using a solid angle filtering [25] method. The local linear fit of [35] is a computation-intensive method, which does not adapt well to the noise from the canopy wall around the trail.…”

“…The rut detection requires a detailed control over the TIN model production, so we do not use the standard methods available from the GIS and point cloud software. Instead, we apply SAF [25], which utilizes the solid angle ω p of the "ground cone" associated with each point p. Filtering is based on having all accepted points within the limits…”

“…a canopy height model to focus the point cloud on the logging trail. The model is produced by a solid angle filtering (SAF) of point clouds [25]; 2. a surface model as a triangularized irregular network (TIN) for trail detection; the model is produced by applying SAF with a different parameter setting, followed by mean curvature flow smoothing (MCF) [26]; 3. ground model vectorization (orientation of possible ruts and likelihood of having a trail through any given point; 4. the height raster of a straightened harvesting trail; this phase uses a histogram of curvatures (HOC) method; 5. a collection of 2D rut profile curves.…”

Debitage and Drones: Classifying and Characterising Neolithic Stone Tool Production in the Shetland Islands Using High Resolution Unmanned Aerial Vehicle Imagery

“…At the other end of the spectrum, 3D scanning using desktop and terrestrial laser scanning have been used to capture surface features on monuments and minute microware patterns on artefacts [28]. Such tools have been used to explore surface rockiness before [29], but while they are highly effective, both techniques are expensive when compared to the models derived using UAV imagery and an SfM pipeline.…”