Separating point clouds into ground and non-ground points is a preliminary and essential
step in various applications of airborne light detection and ranging (LiDAR) data, and many …

We presented a multiresolution hierarchical classification (MHC) algorithm for differentiating
ground from non-ground LiDAR point cloud based on point residuals from the interpolated …

Recent advances in airborne light detection and ranging (LiDAR) technology allow rapid
and inexpensive generation of digital surface models (DSMs), 3-D point clouds of buildings …

Data sets of urban scenes collected from mobile groundbased LiDAR are dense and very
detailed. Automatic extraction of features and objects from such 3D data is desirable since it …

Differential light detection and ranging (LiDAR) from repeated surveys has recently emerged
as an effective tool to measure the three-dimensional (3-D) change. Currently, the primary …

Treetops volume information in groves is a key component of the perception process for
improving herbicide management, foliage density observation and a grove's canopy maturity …

Despite the large amounts of resources destined to developing filtering algorithms of LiDAR
point clouds in order to obtain a Digital Terrain Model (DTM), the task remains a challenge …

The article presents an evaluation of different terrain point extraction algorithms for airborne
laser scanning (ALS) point clouds. The research area covers eight test sites with varying …

Morphological and triangular irregular network (TIN) ground filters require setting up
different parameters to achieve high accuracy for different terrains. A proposed …

Identifying ground points from LiDAR data remains a challenge more than 2 decades after
automatic filtering methods were first developed. The efficacy of filtering methods depends …