Workflow Products

Multi Sensor Fusion Monitoring

Function name: multi_sensor_fusion_monitoring

PROProduction

Workflow-grade Pro analysis with audit-ready outputs.

workflow pro

Workflow Narrative

Multi-Sensor Fusion Monitoring

Problem It Solves

Where do optical and SAR signals jointly support actionable change alerts, and where is confidence high enough to triage immediately?

Who It Is For

  • Multi-source monitoring teams combining optical and SAR evidence.

Primary User

National/regional EO monitoring programs, environmental observatories, and risk intelligence teams.

What It Does

  • Fuses optical change, SAR stability cues, and terrain context into one disturbance-monitoring product.
  • Produces sensor-agreement and fused-probability outputs for confidence-first screening.
  • Emits high-confidence change zones suitable for direct review and reporting.

How It Works

  • Runs remote_sensing_change_detection and sar_analysis_readiness as upstream stages.
  • Computes a per-pixel agreement score from optical confidence plus SAR consistency/coherence cues.
  • Combines normalized change strength and agreement into fused change probability.
  • Indicative formula: $P_{fused} = w_c \cdot |\Delta_{optical}|{norm} + w_a \cdot A{sensor}$.

Why It Wins

  • Prevents one-sensor overconfidence by explicitly encoding cross-sensor agreement into final change probability.

Typical Buying Trigger

Operations teams need lower-false-alarm change monitoring in cloudy/seasonally variable regions where single-sensor methods are unstable.

Typical Presets

  • fast: lower-overhead configuration for broader candidate capture.
  • balanced: default setting for operational triage.
  • conservative: stricter thresholding for high-specificity alerting.

Inputs

ParameterOptionalDescription baseline_bundle, baseline_red_band_index, baseline_nir_band_indexnoBaseline multispectral bundle and red/NIR band selectors used to compute baseline vegetation response. change_bundle, change_red_band_index, change_nir_band_indexnoChange-date multispectral bundle and red/NIR band selectors used for signed change estimation. input_sar, input_demnoSAR scene and terrain model used for radiometric terrain correction and readiness metrics. optional pair_saryesOptional second SAR scene used when pair/coherence diagnostics are enabled. optional thermal_bundle, thermal_band_indexyesOptional thermal raster and 0-based band index used for three-modality diagnostics. profile: fast | balanced | conservativenoProcessing profile controlling sensitivity, quality strictness, and runtime tradeoffs. harmonization_modeyesCross-sensor bias harmonization mode: off, robust, or conservative. high_confidence_threshold, max_zone_featuresnoThreshold and feature-cap controls for extracting high-confidence change zones. vector_output_formatyesOutput vector format for zones: gpkg, geojson, or shp.

Outputs

ParameterTypeDescription fused_change_probabilityGeoTIFFCross-sensor fused probability of meaningful environmental change. sensor_agreementGeoTIFFAgreement surface indicating where sensors support the same change interpretation. terrain_contextGeoTIFFDerived terrain context layer used by fused change interpretation. uncertainty_inflationGeoTIFFPer-pixel uncertainty inflation diagnostic from cross-modality fusion. high_confidence_change_zonesGeoPackageVector zones representing high-confidence change hotspots. thermal_input_contractJSONThermal coverage and weighting contract generated when the workflow runs. modality_contribution_diagnosticsJSONRelative modality contribution diagnostics for optical/SAR/thermal sources. summaryJSONMachine-readable summary report containing run metadata, QA diagnostics, and key metrics. html_reportHTMLHuman-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")

fused, agreement, terrain, uncertainty, zones, thermal_contract, modality_diagnostics, summary = wbe.multi_sensor_fusion_monitoring( baseline_bundle="data/baseline_bundle.tif", baseline_red_band_index=0, baseline_nir_band_index=1, change_bundle="data/change_bundle.tif", change_red_band_index=0, change_nir_band_index=1, input_sar="data/sar_a.tif", input_dem="data/dem.tif", pair_sar="data/sar_b.tif", thermal_bundle="data/thermal.tif", thermal_band_index=0, profile="balanced", harmonization_mode="robust", vector_output_format="gpkg", high_confidence_threshold=0.8, max_zone_features=25000, output_prefix="output/ms_fusion", )

print(fused) print(agreement) print(terrain) print(uncertainty) print(zones) print(thermal_contract) print(modality_diagnostics) print(summary)`

License Notice

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

Guided Uav Image Intake Workflow

Function name: guided_uav_image_intake_workflow

PROProduction

Workflow-grade Pro analysis with audit-ready outputs.

workflow pro

Workflow Narrative

UAV Image Intake QA

Problem It Solves

Is this image set good enough to proceed into expensive downstream processing, and what should be fixed first if not?

Who It Is For

  • UAV mission operators, photogrammetry technicians, and geospatial production teams.

Primary User

Drone services teams, geomatics operations managers, and data-engineering teams responsible for production intake quality.

What It Does

  • Scans UAV imagery and builds a metadata/coverage inventory.
  • Computes intake QA metrics (GPS/EXIF completeness, overlap estimate, image-count sufficiency).
  • Extracts per-image blur scores and flags blurry images with warnings.
  • Parses RTK fix status and gimbal/flight orientation priors from DJI XMP metadata where available.
  • Returns workflow status (pass, review, fail) with warnings suitable for operator triage.

How It Works

  • Recursively discovers supported image files (jpg, jpeg, tif, tiff, png).
  • Parses EXIF fields for timestamp and GPS coordinates; parses DJI XMP sidecar fields for RTK fix status and gimbal yaw/pitch/roll and flight yaw.
  • Estimates overlap from GPS nearest-neighbor spacing against a nominal footprint heuristic.
  • Computes a per-image blur score from a Laplacian variance kernel (configurable as off, fast, or full mode) with wide-SIMD acceleration.
  • Metadata extraction and blur scoring run in parallel across all images using Rayon thread pools.
  • Applies profile thresholds to classify readiness and emit actionable warnings.
  • Indicative heuristic: overlap_proxy ~= 1 - (nn_spacing_m / nominal_footprint_m), clipped to [0, 1].

Why It Wins

  • Converts ad hoc manual preflight checks into a single reproducible QA workflow with machine-readable outputs, including blur quality, RTK status, and orientation priors alongside the standard GPS and overlap diagnostics.

Typical Buying Trigger

Frequent failed/rework-heavy photogrammetry or registration runs caused by avoidable intake quality issues.

Typical Presets

  • fast: permissive intake thresholds with fast-mode blur scoring for rapid field feedback.
  • balanced: default operations profile with fast-mode blur scoring.
  • strict: tighter quality gate with full-mode blur scoring before production processing.

Inputs

ParameterOptionalDescription images_dirnoInput directory containing UAV images to screen. profile: fast | balanced | strictyes[pro] QA profile controlling overlap, metadata readiness, and blur thresholds; drives pass/review/fail classification. Defaults to balanced. recursiveyesIf true, scans subdirectories under images_dir. Defaults to true. output_prefixyesPrefix used to name all output artifacts. blur_mode: off | fast | fullyesBlur scoring mode. off skips blur scoring; fast downsamples before scoring; full scores at original resolution. Defaults to fast.

Outputs

ParameterTypeDescription image_inventoryCSVPer-image inventory with metadata flags, GPS coordinates, capture fields, blur score, and gimbal/RTK orientation columns (*_image_inventory.csv). qa_reportJSONStructured QA checks and warning details for intake triage, including blur and orientation hint sections (*_intake_qa_report.json). summaryJSONWorkflow summary contract with status and aggregate metrics including blur coverage and RTK coverage fractions (*_intake_summary.json). html_reportHTMLHuman-readable customer-facing report generated from the summary contract for stakeholder review and QA traceability. image_centersGeoJSONImage center points from EXIF GPS with per-feature blur score and orientation properties (*_image_centers.geojson). flight_path_linesGeoJSONFlight path line geometry built from ordered image centers with GPS (*_flight_path_lines.geojson).

Python Example

`import whitebox_workflows as wbw

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

result = wbe.guided_uav_image_intake_workflow( images_dir="data/uav_mission/images", profile="balanced", recursive=True, output_prefix="output/uav_intake", blur_mode="fast", )

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.

Registration Oriented Feature Workflow

Function name: registration_oriented_feature_workflow

PROProduction

Workflow-grade Pro analysis with audit-ready outputs.

workflow pro

Workflow Narrative

Image Registration Diagnostics

Problem It Solves

Which pairs are registration-ready, and do we have enough correspondence quality to proceed confidently?

Who It Is For

  • Geospatial registration specialists, EO preprocessing teams, and image-fusion analysts.

Primary User

Teams building repeatable image registration pipelines (RGB-RGB, thermal-RGB, and cross-date alignment).

What It Does

  • Runs image-pair or image-set registration diagnostics using a RootSIFT feature engine.
  • Produces tie-point outputs and pair-level quality metrics with full fallback attempt traces.
  • Handles cross-modal pairs (e.g., thermal LWIR to RGB visible) via automatic histogram equalization preprocessing.
  • Optionally emits annotated side-by-side pair visualizations with tie-point overlays.
  • Emits workflow-level readiness status for registration-first pipelines.

How It Works

  • Builds pair candidates (single pair mode or ranked set mode).
  • Extracts RootSIFT descriptors from a Gaussian scale-space pyramid; descriptor distances are computed with wide-SIMD acceleration.
  • Applies cross-verified nearest-neighbor matching with ratio test filtering.
  • On match shortfall, escalates through a six-strategy fallback chain: baseline → high_feature_baseline → relaxed_ratio → high_feature_relaxed → preprocess_eq_right → preprocess_eq_both; the final two strategies apply histogram equalization to recover keypoints from low-contrast inputs such as LWIR thermal imagery.
  • Records the full strategy attempt trace per pair in diagnostics for auditable QA.
  • Reports per-pair keypoint counts, match counts, confidence summary, inlier-style proxy metrics, strategy used, and fallback policy.
  • Indicative rule: accept match if d1 / d2 ParameterOptionalDescription mode: set | pairnoExecution mode for set-wide pair planning or explicit pair diagnostics. images_dirno (set mode)Input image directory used when mode is set. left_image, right_imageno (pair mode)Explicit pair inputs used when mode is pair. max_pairsyesMaximum number of candidate pairs evaluated in set mode. max_features_per_imageyesUpper bound on extracted keypoints per image. ratio_testyesDescriptor ratio-test threshold controlling match strictness. min_matchesyes[pro] Minimum accepted match count per pair; drives the QA gate, fallback routing, and pass/review/fail classification. output_prefixyesPrefix used to name workflow outputs. emit_pair_match_vizyesIf true, writes annotated side-by-side pair images with tie-point lines. Defaults to false. max_pair_visualizationsyesMaximum number of pair visualizations to write when emit_pair_match_viz is true. Defaults to 8. max_lines_per_pairyesMaximum number of tie-point lines drawn per visualization. Defaults to 150. viz_scaleyesDownscale factor applied to visualization images, in [0.05, 1.0]. Defaults to 0.5.

Outputs

ParameterTypeDescription pair_diagnosticsJSONPair-level diagnostics including candidate score, keypoint counts, match quality, confidence proxies, strategy used, fallback flag, and full strategy attempt trace (*_pair_diagnostics.json). match_summaryJSONWorkflow summary contract with aggregate match metrics, status, and fallback policy record (*_match_summary.json). html_reportHTMLHuman-readable customer-facing report generated from the workflow summary contract for stakeholder review and QA traceability. tie_pointsCSVTie-point table (pair_id,left_x,left_y,right_x,right_y,confidence) for downstream registration workflows (*_tie_points.csv). pair_match_vizdirectoryAnnotated side-by-side JPEG images per pair with tie-point lines overlaid, written when emit_pair_match_viz is true.

Python Example

`import whitebox_workflows as wbw

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

result = wbe.registration_oriented_feature_workflow( mode="set", images_dir="data/uav_mission/images", max_pairs=24, max_features_per_image=500, ratio_test=0.80, min_matches=24, output_prefix="output/registration_workflow", emit_pair_match_viz=True, )

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.