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Classifying the original point clouds into ground and non-ground points is a key step in LiDAR (light detection and ranging) data post-processing. Cloth simulation filtering (CSF) algorithm, which bas...
In the present study, we applied the Particle Swarming Optimization (PSO) procedure to parametrize two Local Maxima (LM) algorithms and a pattern recognition model based on raster and point-cloud data...
Data storage capacity and high processing speed available today has made it possible to digitally sample and store the entire reflected waveform of Small-Footprint Airborne Lidar (light detection and ...
Airborne LiDAR technology has proven to be the most powerful tools to obtain high-density, high-accuracy and significantly detailed surface information of terrain and surface objects within a short ...
When full-waveform LiDAR (FW-LiDAR) data are applied to extract the component feature information of interest targets, there exist a problem of components lost during the waveform decomposition proced...
Light Detection and Ranging (LIDAR) systems have become a standard data collection technology for capturing object surface information and 3D modeling of urban areas. Although, LIDAR systems provide...
One of the fundamental steps in the transformation of the LIDAR data into the meaningful objects in urban area involves their segmentation into consistent units through a clustering process. Neverth...

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