Describe the procedures of classifying LiDAR point cloud data and the difference between the clusters and the meaningful classes.
LiDAR point cloud analysis refers to the techniques and methods used to explore LiDAR point cloud data in order to extract and visualize specific information about target features, such as land surface topography, lake bathymetry, vegetation canopy height, etc. In general, the procedures involved include noise filtering, point classification, feature extraction, quantification, and 3D reconstruction. The sequence of the procedures and the method used in each procedure can vary based on the nature of the LiDAR point cloud data, the application scenario, and the required accuracy. Recent advancements in technology, particularly drone technology, have made LiDAR data collection easier and more cost-effective. With the widespread availability of LiDAR data, point cloud analysis is facilitated by various software tools and is applied in many different domains, including urban planning, forestry inventory, biomass mapping, topography visualization, and environmental monitoring, etc.