What is Point Cloud Classification?

Point cloud classification is the process of labeling each point in a LiDAR dataset with a category — ground, vegetation, buildings, power lines, water, and more. Without classification, you have millions of anonymous XYZ coordinates. With it, you have actionable intelligence about every feature in your survey area.

How Classification Works

Traditional classification uses rule-based algorithms that analyze geometric relationships between points. Ground classification, for example, looks for relatively flat surfaces at the lowest local elevations. Vegetation appears as irregular clusters above the ground surface. Buildings show planar surfaces with sharp edges.

The challenge is that rules tuned for one environment often fail in another. Parameters optimized for flat farmland produce terrible results in mountainous terrain. Urban settings with complex architecture confuse algorithms trained on natural landscapes. This is where machine learning changes the game.

AI-powered classification learns patterns from labeled training data rather than following rigid rules. Neural networks process point neighborhoods in 3D, recognizing features that would be impossible to capture in hand-crafted rules. The result is classification that adapts to diverse environments without manual parameter adjustment.

Why It Matters

Every downstream product depends on classification quality. Terrain models require accurate ground identification. Vegetation analysis needs reliable separation of trees from structures. Power line inspection demands precise detection of wires and towers. Poor classification cascades into poor deliverables, while accurate classification enables analysis that would be impossible with unclassified data.

Classification with LidarVisor

LidarVisor uses deep learning models trained on diverse global datasets. Upload your LAS or LAZ file and receive classified results in minutes — ground, vegetation, buildings, and infrastructure labeled automatically and ready for analysis.