Point Cloud Cleaning

Raw LiDAR captures everything, including vehicles, equipment, and temporary objects that contaminate your terrain model. Automatic classification removes these artifacts, revealing the true landscape.

If these objects remain in your data, they contaminate derivative products. Your DTM shows artificial bumps where cars were parked. Contour lines warp around truck beds. Building footprints include vehicle outlines.

The solution lies in classification: assigning each point to a category based on what surface it represents. Standard LiDAR classifications include:

• Ground: Bare earth surface
• Vegetation: Trees, shrubs, grass
• Building: Rooftops and structures
• Vehicle: Cars, trucks, mobile objects
• Noise: Atmospheric interference, outliers

Once points are classified, filtering is straightforward. Remove the vehicle class, and those temporary objects disappear, revealing the ground beneath.

Manual point cloud cleaning is tedious and error-prone. In a typical urban dataset, thousands of individual points may belong to vehicles scattered across the scene.

Manual selection takes hours,and operators inevitably miss objects or accidentally remove valid ground points.

Lidarvisor identifies vehicles based on their geometric characteristics:

• Height above ground: Vehicles sit 0.2–2.5m above the surface
• Shape patterns: Rectangular footprints, curved rooflines
• Context: Located on roads, parking areas, driveways
• Isolation: Distinct from surrounding ground returns

The algorithms analyze these characteristics across your entire point cloud, identifying and tagging vehicles automatically.

• ✅ Clean data in minutes, not hours
• ✅ Consistent results across large datasets
• ✅ No manual point selection required
• ✅ Ground points preserved beneath vehicles

• High Noise Points: Atmospheric interference, birds, and sensor artifacts create scattered outliers far above or below terrain. Classification identifies and removes them.
• Roof Objects: HVAC units, solar panels, and rooftop equipment can be separated from the main building structure.
• Moving Objects: Vehicles in motion create distinctive artifacts: elongated shapes, ghosted duplicate returns. The algorithms recognize and handle these appropriately.