A New Recursive Filtering Method of Terrestrial Laser Scanning Data to Preserve Ground Surface Information in Steep-Slope Areas

Abstract

Landslides are one of the critical natural hazards that cause human, infrastructure, and economic losses. Risk of catastrophic losses due to landslides is significant given sprawled urban development near steep slopes and the increasing proximity of large populations to hilly areas. For reducing these losses, a high-resolution digital terrain model (DTM) is an essential piece of data for a qualitative or a quantitative investigation of slopes that may lead to landslides. Data acquired by a terrestrial laser scanning (TLS), called a point cloud, has been widely used to generate a DTM, since a TLS is appropriate for detecting small-to large-scale ground features on steep slopes. For an accurate DTM, TLS data should be filtered to remove non-ground points, but most current algorithms for extracting ground points from a point cloud have been developed for airborne laser scanning (ALS) data and not TLS data. Moreover, it is a challenging task to generate an accurate DTM from a steep-slope area by using existing algorithms. For these reasons, we developed an algorithm to automatically extract only ground points from the point clouds of steep terrains. Our methodology is focused on TLS datasets and utilizes the adaptive principal component analysis-triangular irregular network (PCA-TIN) approach. Our method was applied to two test areas and the results showed that the algorithm can cope well with steep slopes, giving an accurate surface model compared to conventional algorithms. Total accuracy values of the generated DTMs in the form of root mean squared errors are 1.84 cm and 2.13 cm over the areas of 5252 m 2 and 1378 m 2 , respectively. The slope-based adaptive PCA-TIN method demonstrates great potential for TLS-derived DTM construction in steep-slope landscapes.

TLS Measurement during Static Load Testing of a Railway Bridge

“…Surface models of point clouds are an adequate solution for surface objects such as cooling towers [40], dams [41][42], or tunnels [43]. The comparison of periodic surface models of an object in order to detect dislocation is a recognised procedure in landslide monitoring [44][45] and determining the progress of erosion of rocky cliffs [46][47][48]. The investigation into displacement and deformation of natural objects does not, however, require as great an accuracy as work with civil structures.…”

An Analysis of Ground-Point Classifiers for Terrestrial LiDAR

“…A possible explanation for the increased accuracy of this type of classifier over surface-based classifiers was the increased detail of the ground surfaces captured by TLS. While ALS data can provide a more consistent coverage of the ground surface throughout the entire scanned scene, TLS provides a far more detailed characterization of the surface and the surrounding area [72]. This can be problematic when scanning grassed landscapes as it can add an unnecessary amount of complexity to the ground surface.…”

A new point cloud processing method unveiled hidden coastal boulders from deep vegetation

“…Although filtering vegetation is commonly used to create a digital elevation model (DEM) from a digital surface model (DSM), the conventional method of filtering vegetation targets the 2.5-dimensional shape: a representation of the three-dimensional shape as seen from a single direction. The method is therefore unsuitable to filter out vegetation for three-dimensional (3D) shapes 18 , 19 . The vegetation covering the boulders, which are truly 3D shapes, cannot be filtered out using conventional filtering methods.…”