LiDAR QA and Confidence
What It Does
LiDAR QA and Confidence runs a full QA pass for LiDAR ground-surface readiness and produces confidence diagnostics, uncertainty products, QA flags, hotspot outputs, and a summary report for acceptance decisions.
Typical Questions This Tool Helps Answer
- Does this LiDAR delivery meet the density, accuracy, and classification standards required to proceed to DEM or canopy product generation?
- Which flight lines or spatial areas have inadequate coverage, low point density, or classification inconsistencies that must be resolved before use?
- Can we accept this dataset and move to downstream processing, or does it need formal remediation or re-delivery from the contractor?
Background
LiDAR QA and confidence assessment is a data-readiness gate that quantifies whether a delivery can support downstream terrain, vegetation, and corridor workflows with defensible uncertainty bounds.
A useful framing is a composite confidence function over diagnostics:
$$C(x)=g\left(\text{density}(x), \text{classification_consistency}(x), \text{surface_residual}(x), \text{artifact_flags}(x)\right)$$
Where lower confidence indicates higher risk that derived products are unstable or acceptance-sensitive.
QA Dimensions
- Coverage and density adequacy.
- Classification consistency and plausibility.
- Surface residual quality relative to expected terrain behavior.
- Artifact prevalence and hotspot concentration.
These dimensions support pass/review/reprocess gating with auditable rationale.
Methodological Considerations
- Treat profile mode (
strict,balanced,permissive) as governance policy, not cosmetic tuning. - Use checkpoint validation when available to ground vertical confidence in external control.
- Compare hotspot severity and fail fraction together before acceptance decisions.
Practical Interpretation Pitfalls
Typical misreads include using mean confidence without hotspot context, interpreting fast-mode outputs as full QA equivalence, and approving borderline datasets without documenting residual risk.
Source-Verified Contract
Tool id: lidar_qa_and_confidence
Required Input
| Parameter | Required | Description |
|---|---|---|
input | Yes | Input LiDAR file path. |
Optional Settings
| Parameter | Default | Description |
|---|---|---|
qa_mode | balanced | One of strict, balanced, permissive, auto (auto runs balanced profile). |
block_size | 1.0 | Grid cell size in xy units. |
max_building_size | 150.0 | Maximum expected object width in xy units. |
slope_threshold | 15.0 | Minimum edge slope in degrees. |
elev_threshold | 0.15 | Reference-surface threshold in z units. |
high_confidence_threshold | 0.8 | High-confidence threshold for summary metrics (must be in [0, 1]). |
adapt_by_slope | true | Enable slope-adaptive tolerance behavior. |
adapt_by_roughness | true | Enable roughness-adaptive tolerance behavior. |
density_window | 5 | Local confidence-density window size; odd and >= 3. |
roughness_window | 5 | Local roughness window size; odd and >= 3. |
hotspot_min_cells | 9 | Minimum connected hotspot size in cells; must be >= 1. |
hotspot_rank_top_n | unset | Optional cap on ranked hotspots; if set must be >= 1. |
checkpoints | unset | Optional checkpoint point layer path for vertical validation. |
checkpoint_z_field | unset | Optional checkpoint elevation field name. |
fast_mode | false | Quick exploratory mode; skips hotspot extraction/ranking, stratified metrics, and checkpoint validation. |
output_prefix | lidar_qa | Prefix for generated QA artifacts. |
output | unset | Optional output path for classified/filtered LiDAR result. |
Output Artifacts
| Output Key | Description |
|---|---|
classified_lidar | Classified/filtered LiDAR output path. |
dtm | Generated QA DTM raster. |
confidence | Confidence raster. |
uncertainty | Uncertainty raster. |
uncertainty_rel | Relative uncertainty raster. |
qa_flags | QA flags raster. |
qa_hotspots | Hotspot GeoPackage output. |
summary | Summary JSON report. |
html_report | HTML report path. |
Important Fast Mode Note
Even in fast mode, the workflow still emits a qa_hotspots artifact path. The hotspot layer is written as empty rather than omitted.
Example: Standard QA
import whitebox_workflows as wbw
wbe = wbw.WbEnvironment()
wbe.lidar_qa_and_confidence(
input="site_delivery.laz",
qa_mode="balanced",
output_prefix="site_delivery_qa",
output="site_delivery_classified.laz"
)
Example: Compliance-Oriented QA
wbe.lidar_qa_and_confidence(
input="contract_block_11.laz",
qa_mode="strict",
elev_threshold=0.12,
high_confidence_threshold=0.85,
checkpoints="contract_block_11_checkpoints.gpkg",
checkpoint_z_field="z_control",
hotspot_rank_top_n=20,
output_prefix="contract_block_11_qa",
output="contract_block_11_classified.laz"
)
Example: Fast Intake Triage
wbe.lidar_qa_and_confidence(
input="incoming.laz",
qa_mode="auto",
fast_mode=True,
output_prefix="incoming_quickqa"
)
When To Use This Workflow
- You need a single QA workflow before downstream terrain or corridor products.
- You need repeatable confidence and uncertainty diagnostics for acceptance reviews.
- You need consistent report artifacts for operations, audit, or vendor feedback.
Results Delivery Checklist
- Confirm summary JSON (
summary) exists and is archived. - Confirm confidence/uncertainty/QA-flag rasters are generated.
- Confirm hotspot artifact exists for every run (including fast mode).
- Confirm optional checkpoints are only configured when checkpoint data is available.
Common Questions
Does this workflow require checkpoints?
No. checkpoints is optional.
What is the best default mode?
balanced is the default and a good starting point for most production QA.
Can I reduce runtime for exploratory checks?
Yes. Set fast_mode=true.
Does qa_mode=auto pick a unique model?
No. auto currently resolves to balanced behavior.
Related Workflows
lidar_terrain_product_suitelidar_change_and_disturbance_analysis