Point Cloud Classification

Point Cloud Classification is an important process in LiDAR processing, where the 3D point cloud data are classified into different groups depending on the actual object represented by each point. This way of processing LiDAR data makes it possible to classify points into several categories including ground, buildings, vegetation, roads, bridges, water, and other categories and turn raw data into valuable geospatial information. Proper classification allows for the use of this technology in 3D mapping, surveying, terrain modeling, digital elevation models (DEMs), digital surface models (DSMs), infrastructure assessment, forestry, urban planning, and environmental monitoring. Automated and machine learning-based classification processes allow for more efficient processing of LiDAR data and managing large amounts of data. Point clouds can be filtered for creating bare earth terrains, building extraction, vegetation extraction, and performing spatial analysis. The use of point cloud classification is widespread in GIS, remote sensing, drone mapping, aerial LiDAR, mobile mapping, and geospatial data analysis.