Reliable classes for terrain and analysis
ALTIVUE GEOSPATIAL SERVICE 04
LiDAR Classification Services
Assign point classes against agreed definitions, with review of terrain, ambiguous objects and classification errors.
Discuss this service ↗︎THE CLIENT OUTCOME
Classes that support terrain, analysis and extraction with fewer corrections.
Custom class tables aligned to your workflow
Human review of ambiguity and systematic errors
BEST FIT
When to bring in this service.
- LiDAR producers needing classification overflow
- Teams correcting automated or legacy classifications
- Utility, forestry and terrain programs with custom classes
Bare-earth production
Ground separated from vegetation, structures, bridges, noise and other non-terrain returns.
Land-cover separation
Vegetation, buildings, water and project-specific features prepared for mapping and analysis.
Utility classification
Poles, conductors and elevated infrastructure classified where density and geometry make them reliably identifiable.
DELIVERABLES
Standard or custom classes, resolved against real terrain context.
LiDAR classification services for ground, vegetation, buildings, water, utilities, noise and custom LAS classes.
01
Ground and non-ground classification
02
Low, medium and high vegetation
03
Building and structure classes
04
Water and bridge classes; project-specific flag rules
05
Utility poles and wires
06
Noise and withheld-point review
CALIBRATED WORKFLOW
Production workflow
Every project begins with a representative sample and measurable acceptance criteria.
Profile
Measure density, scan characteristics, terrain, land cover and existing class use.
Calibrate
Approve a representative tile and lock class definitions and exception handling.
Classify
Use automated processing with systematic manual correction and targeted 3D review.
Audit
Check class confusion, artifacts, omissions, tile seams and metadata before release.
ACCEPTANCE CONTROLS
Checks to agree with your reviewers.
Checks are selected from the actual deliverable specification and confirmed during calibration.
- Class confusion sampling
- Ground-surface artifact review
- Noise and withheld-point checks
- Seams, metadata and custom-class use
01 / INPUTLAS/LAZ, class table and accepted examples02 / ALTIVUE GEOSPATIALAutomated classification plus targeted 3D correction03 / HANDOFFReviewed classified point clouds and exceptions
INPUTS TO ASSESS
Work with the data you already capture.
Start with a source inventory and a representative sample. Confirm transfer arrangements before sharing restricted data.
Georeferenced LAS or LAZPartially classified dataASPRS or custom class tableHydro and boundary dataRepresentative acceptance examples
YOUR ACCEPTANCE REVIEW
Judge the classes against an agreed reference sample.
Include the class and flag specification, per-tile class summaries, review findings and unresolved areas with the classified files.
- Compare representative terrain and land cover with agreed reference labels
- Review omission and commission by relevant class, with the sample size stated
- Check ground-derived surfaces, tile boundaries and class/flag combinations
AGREE BEFORE PRODUCTIONTarget LAS version and point format, class table, flag rules, vegetation thresholds, terrain edge cases and acceptance thresholds.SCOPE LIMITClassification changes labels, not source positional accuracy. Small wires, low objects and obscured ground may remain unresolved where returns are insufficient. Project-specific flag rules take precedence over generic presets.REVIEW AND CORRECTIONSet the review window, defect reporting format and correction responsibilities in the scope. Record approved exceptions separately from items requiring rework.
FREQUENT QUESTIONS
Before you send a sample.
Do you provide LAS classification and reclassification?+
Yes. Altivue Geospatial classifies raw data and corrects existing classified LAS/LAZ against standard or custom class definitions.
Can poles and wires be classified?+
Yes, where point density and geometry support reliable identification. The pilot defines what is consistently attainable.
How is classification quality measured?+
Quality controls include class-use validation, systematic visual review, targeted sampling, surface checks and documented exceptions.
START WITH ONE REPRESENTATIVE AREADiscuss the scope
Discuss the scope
and required outputs.
Share the approximate volume, required outputs and acceptance criteria. Altivue Geospatial will define a focused production pilot.