Separating Ground from Above
A raw LiDAR point cloud contains returns from every surface the laser pulse encounters: treetops, building roofs, power lines, vehicles, and the ground itself. Separating ground from non-ground is the critical first step in producing bare-earth Digital Elevation Models used for flood mapping, infrastructure planning, and archaeological prospection. The challenge is that no single rule perfectly distinguishes a steep hillside from a building wall, or a boulder from a car — making this one of the most studied problems in geospatial science.
Progressive Morphological Filtering
The most widely used approach builds the ground surface iteratively from coarse to fine. Starting with large grid cells, the algorithm identifies the lowest point in each cell as a ground seed. It then progressively decreases cell size, adding points that satisfy slope and height-difference criteria relative to the existing ground surface. Points too far above the interpolated surface are classified as non-ground. This simulation visualizes the classification process and shows how parameter choices affect the resulting ground surface.
Error Trade-offs
Every classification algorithm faces a fundamental trade-off between commission errors (falsely including non-ground objects) and omission errors (falsely rejecting ground points). Relaxing thresholds captures more legitimate ground but also admits low vegetation and terrain-following structures. Tightening thresholds cleans the ground surface but erodes edges, clips hilltops, and removes rough terrain features. The simulation lets you explore this trade-off interactively by adjusting slope, height, and cell-size parameters.
Adaptive Classification
Modern production workflows use spatially varying parameters: tight thresholds in flat urban areas (where buildings must be excluded) and relaxed thresholds in mountainous terrain (where steep slopes are legitimate ground). Machine learning approaches trained on manually classified reference datasets now augment or replace rule-based filtering in complex environments. The goal remains the same: extract an accurate bare-earth surface that faithfully represents the terrain beneath all surface cover.