LiDAR Classification Guide: ASPRS Classes, Codes, and Point Cloud Workflow

This guide explains ASPRS class codes plus the workflow teams use to turn LAS or LAZ files into terrain, forestry, and utility-ready deliverables.

Without classification, a point cloud is simply millions of XYZ coordinates. With classification, it becomes an organized dataset where you can:

• Isolate ground points for terrain modeling
• Extract buildings for urban mapping
• Identify vegetation for forestry analysis
• Map power lines for utility inspection

Classification information is stored within LAS/LAZ files as an integer attribute following ASPRS standards.

• 0 — Never Classified
• 1 — Unclassified
• 2 — Ground
• 7 — Low Point (Noise)
• 9 — Water

• 3 — Low Veg (0-0.5m)
• 4 — Medium Veg (0.5-2m)
• 5 — High Veg (>2m)
• 6 — Building

• 10 — Rail
• 11 — Road Surface
• 14 — Wire Conductor
• 15 — Transmission Tower
• 17 — Bridge Deck

In agricultural landscapes, classification separates cropland, hedgerows, farm buildings, and irrigation infrastructure. This enables precision farming analysis and land management planning.

Always classify noise first. Outlier points corrupt subsequent algorithms — high noise (birds, atmosphere), low noise (multipath errors).

Ground classification is foundational. Building and vegetation classification require knowing where the ground is. Common algorithms: Progressive TIN Densification, Cloth Simulation Filter (CSF), SMRF.

Algorithms identify buildings based on height above ground, planar surfaces, and geometric regularity.

Separate points into height strata: Low (0-0.5m), Medium (0.5-2m), High (>2m). Distinguishing vegetation from buildings relies on scattered patterns vs planar surfaces.

Classifying rural areas with mixed forest coverage requires careful parameter tuning. Ground classification algorithms must handle the transition between open fields and dense canopy, while vegetation stratification reveals the forest structure from understory to crown.

Power lines and transmission infrastructure require specialized classification algorithms. The thin linear geometry of conductors demands high point density and careful separation from surrounding vegetation. Classified power line data enables encroachment analysis and maintenance planning.

LidarVisor uses AI-powered classification to automatically label ground, vegetation, buildings, and more. Upload your LAS/LAZ files and get classified data with DTM/DSM generation.

Built-in manual tools (brush selection, class permutation, filtering) let you refine edge cases without leaving the platform.