Inspect the source
Check point density, registration, metadata, class use and coverage.
LiDAR Processing
Clean, classify and organize captured LiDAR for terrain modelling, feature extraction and downstream CAD or GIS production. Align the class rules and review criteria with your team.
PROJECT INPUTSStart with the source.
Start with the source.
Define the result.
LAS / LAZ or registered point clouds, source metadata, project class definitions, horizontal and vertical references and accepted example data.
Discuss this capability ↗︎POTENTIAL DELIVERABLES
- Classified LAS / LAZ point clouds
- Terrain and surface models
- Contours and scoped breaklines
- Tile-level QA and exception records
Production checks and file validity do not establish absolute positional accuracy. Accuracy assessment requires suitable independent reference observations and a defined method.
PRODUCTION WORKFLOW
Make each step reviewable.
A defined process connects your source data to the agreed acceptance criteria.
Calibrate classification
Approve a representative tile with clear rules for ground, vegetation and infrastructure.
Process to specification
Apply cleanup, classification, tiling and agreed terrain treatments.
Validate the handover
Check outputs against the class table, surface requirements and acceptance plan.
QUALITY & HANDOVER
Know what was checked.
Select the checks, coverage, tolerances and evidence appropriate to your project.
Explore our quality approach ↗︎- Point records, flags and class definitions
- Noise, ground interpretation and tile continuity
- Surface voids and unsupported areas
- Coordinate references, units and agreed accuracy evidence
Bring the brief. We’ll work through the scope.
Share your source type, approximate extent, intended use and required outputs.