PRO Production

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 Support WbW