A point cloud is the native product of LiDAR (and dense photogrammetry): millions to billions of 3D points, each carrying coordinates plus attributes — intensity, return number, timestamp, and after processing, a classification code. Classification turns an undifferentiated cloud into structured data: ground here, vegetation there, buildings, water, wires — the step on which every derived product depends.
Anatomy of the data
The LAS format (and its compressed twin LAZ) is the industry standard, storing per-point attributes and standardised classification codes maintained by ASPRS: ground, low/medium/high vegetation, building, water, rail, road, wire classes and more. Projects add bespoke classes — conductor versus shield wire, pylon components — as specifications demand.
Point density is the resolution analogue: 2 points/m² supports terrain modelling; 30+ supports building reconstruction; hundreds enable engineering-detail extraction.
How classification is actually done
Automated routines do the heavy lifting: morphological and TIN-based ground filters, geometric rules and increasingly deep learning for structures. But automation degrades exactly where products matter most — steep terrain, dense canopy, complex urban fabric, wires — so production workflows finish with skilled manual editing.
The realistic split on demanding specifications is substantial manual refinement, which is why per-tile QC dashboards, class-accuracy sampling and documented editing standards distinguish production vendors from script-runners.
What classified clouds unlock
From ground points: DTMs, contours, hydro-enforced surfaces. From vegetation: canopy height, biomass, fuel loads, encroachment against clearance envelopes. From buildings: footprints and 3D models. From wires: catenary models for powerline engineering.
Every one of these inherits the classification’s errors — a misclassified berm becomes a phantom flood barrier — which is why accuracy reporting per class, not just overall, is the professional norm.
Frequently asked questions
What are the standard LiDAR classification classes?
The ASPRS LAS specification defines codes including ground (2), low/medium/high vegetation (3–5), building (6), water (9), and reserved classes for rail, roads, wires and structures — with project-specific extensions common in utility and corridor work.
How is LiDAR classification accuracy measured?
By sampling: independent reviewers check point labels across stratified samples, reporting per-class accuracy and systematic errors. Contracts typically specify thresholds per class, verified tile by tile.
Need this done professionally?
GLOBEIR delivers this work for government and enterprise clients.