PROProduction

Assess LiDAR point-cloud quality and compute confidence metrics for terrain extraction readiness.

workflow pro

Workflow Narrative

LiDAR QA and Confidence

Problem It Solves

Is this LiDAR deliverable trustworthy enough for production terrain modeling, and where are the risk zones?

Who It Is For

LiDAR production QA teams and data acceptance reviewers.

Primary User

Survey/mapping firms, government mapping programs, and enterprise geospatial platforms.

What It Does

Runs LiDAR QA workflow with ground-surface diagnostics.
Produces confidence, uncertainty, and QA flags for acceptance screening.
Supports qa_mode-driven strictness behavior (strict, balanced, permissive, auto).
Supports fast_mode for exploratory runs that skip non-critical diagnostics.

How It Works

Classifies and normalizes point-cloud structure to estimate ground-surface consistency.
Builds rasterized QA metrics (confidence, uncertainty, flags) from neighborhood evidence.
Applies mode-specific acceptance thresholds to summarize pass-risk patterns.
Optionally runs checkpoint vertical validation and auto-mode recommendations in summary outputs.
Indicative formula: confidence ~= 1 - normalized(local_residual + return_structure_penalty).

Why It Wins

Converts QA from a binary pass/fail decision into spatial confidence and uncertainty diagnostics.

Typical Buying Trigger

Data acceptance teams need objective QA evidence before approving vendor LiDAR for production use.

Typical Presets

strict for conservative acceptance checks.
balanced for normal production QA.
permissive for exploratory preprocessing.
auto for recommendation-guided QA mode selection.
fast_mode=true for rapid exploratory runs when full diagnostics are not required.

Inputs

Parameter	Optional	Description
input (LAS/LAZ)	no	Input LiDAR point cloud used to derive QA, terrain, structure, or encroachment products.
qa_mode and QA threshold controls	no	QA strictness mode and threshold controls for LiDAR acceptance diagnostics.
fast_mode	yes	Optional acceleration mode that skips hotspot extraction, stratified metrics, and checkpoint validation for faster exploratory runs.

Outputs

Parameter	Type	Description
classified_lidar	optional LAS/LAZ	Optional classified LiDAR point cloud output from QA/terrain workflows.
dtm	GeoTIFF	Digital terrain model raster generated from workflow processing.
confidence	GeoTIFF	Confidence layer quantifying reliability of modeled outputs.
uncertainty	GeoTIFF	Uncertainty diagnostics layer highlighting low-certainty areas.
qa_flags	GeoTIFF	QA flag raster identifying cells that failed quality checks.
summary	JSON	Machine-readable summary report containing run metadata, QA diagnostics, and key metrics.
html_report	HTML	Human-readable customer-facing report generated from the summary contract for stakeholder review and QA traceability.

Python Example

import whitebox_workflows as wbw

wbe = wbw.WbEnvironment(include_pro=True, tier="pro")

result = wbe.lidar_qa_and_confidence(
    input="data/points.laz",
    qa_mode="balanced",
    fast_mode=False,
    output_prefix="output/lidar_qa",
)

print(result)

License Notice

Use of this function requires a license for Whitebox Workflows Professional (WbW-Pro). Please visit www.whiteboxgeo.com to purchase a license.

Project Links

WbW Homepage User Manual Learn More