drone-inspection-specialist

Drone Inspection Specialist

Expert in drone-based infrastructure inspection with computer vision, thermal analysis, and 3D reconstruction for insurance, property assessment, and environmental monitoring.

Decision Tree: When to Use This Skill

User mentions drones/UAV?
├─ YES → Is it about inspection or assessment of something?
│        ├─ Fire detection, smoke, thermal hotspots → THIS SKILL
│        ├─ Roof damage, hail, shingles → THIS SKILL
│        ├─ Property/insurance assessment → THIS SKILL
│        ├─ 3D reconstruction for measurement → THIS SKILL
│        ├─ Wildfire risk, defensible space → THIS SKILL
│        └─ NO (flight control, navigation, general CV) → drone-cv-expert
└─ NO → Is it about fire/roof/property assessment without drones?
        ├─ YES → Still use THIS SKILL (methods apply)
        └─ NO → Different skill needed

Core Competencies

Fire Detection & Wildfire Risk

• Multi-Modal Detection: RGB smoke + thermal hotspot fusion
• Precondition Assessment: NDVI, fuel load, vegetation density
• Defensible Space: CAL FIRE/NFPA 1144 compliance evaluation
• Progression Tracking: Spread rate, direction prediction

Roof & Structural Inspection

• Damage Detection: Cracks, missing shingles, wear, ponding
• Hail Analysis: Impact pattern recognition, size estimation
• Thermal Analysis: Moisture detection, insulation gaps, HVAC leaks
• Material Classification: Asphalt, metal, tile, slate identification

3D Reconstruction (Gaussian Splatting)

• Pipeline: Video → COLMAP SfM → 3DGS training → Web viewer
• Measurements: Roof area, damage dimensions, property bounds
• Change Detection: Before/after comparison for claims

Insurance & Reinsurance

• Claim Packaging: Documentation meeting industry standards
• Risk Modeling: Catastrophe models, loss distributions
• Precondition Data: Satellite + drone + ground integration

Anti-Patterns to Avoid

1. "Single-Sensor Dependence"

Wrong: Using only RGB for fire detection. Right: Multi-modal fusion (RGB + thermal) for high-confidence alerts.

Detection Source	Confidence	Action
Thermal fire only	70%	Alert + verify
RGB smoke only	60%	Alert + investigate
Thermal + RGB	95%	Confirmed fire

2. "Ignoring Hail Pattern"

Wrong: Counting damage without analyzing spatial distribution. Right: True hail damage has RANDOM distribution. Linear or clustered patterns indicate other causes (foot traffic, age).

3. "Thermal Temperature Trust"

Wrong: Using raw thermal values without calibration. Right: Account for:

• Emissivity of materials (roof = 0.9-0.95)
• Atmospheric transmission (humidity, distance)
• Reflected temperature from surroundings
• Time of day (thermal lag)

4. "3DGS Frame Overload"

Wrong: Extracting every frame from drone video. Right: Extract 2-3 fps with 80% overlap. More frames ≠ better reconstruction.

Video FPS	Extract Rate	Result
30	30 (all)	Redundant, slow processing
30	2-3	Optimal quality/speed
30	0.5	Insufficient overlap

5. "Insurance Claim Speculation"

Wrong: Estimating costs without material identification. Right: Identify material → Apply correct cost matrix.

Material	Repair $/sqft	Replace $/sqft
Asphalt shingle	$5-10	$3-7
Metal	$10-15	$8-14
Tile	$12-20	$10-18
Slate	$20-40	$15-30

6. "Defensible Space Zone Confusion"

Wrong: Treating all vegetation equally regardless of distance. Right: CAL FIRE zones have different requirements:

Zone	Distance	Requirement
0	0-5 ft	Ember-resistant (no combustibles)
1	5-30 ft	Lean, clean, green (spaced trees)
2	30-100 ft	Reduced fuel (selective thinning)

Data Collection Strategy

Satellite Data (Regional Context)

• Sentinel-2: 10m resolution, NDVI, fuel moisture (SWIR bands)
• Landsat-8: 30m resolution, historical baseline, thermal band
• Planet: 3m resolution daily, change detection
• Application: Regional risk mapping, before/after events

Drone Data (Property Detail)

• RGB Mapping: 2-5cm GSD, orthomosaic, 3D model
• Thermal Survey: Moisture detection, heat signatures
• Close Inspection: Damage documentation, detail photos
• Application: Individual property assessment

Ground Truth

• Slope Measurement: GPS transects for topographic risk
• Soil Sampling: Moisture content for fire risk
• Material Verification: Confirm roof type
• Application: Calibration and validation

Quick Reference Tables

Fire Detection Confidence Levels

Signal Combination	Confidence	Alert Priority
Thermal >150°C + Smoke	95%	CRITICAL
Thermal fire model	80%	HIGH
Hotspot >80°C	70%	MEDIUM
Smoke only	60%	MEDIUM
Hotspot 60-80°C	50%	LOW

Roof Damage Severity

Type	Low	Medium	High	Critical
Missing shingle	-	-	Always	-
Crack	<1"	1-3"	>3"	Multiple
Granule loss	<10%	10-30%	>30%	-
Ponding	-	Small	Large	Active leak

Wildfire Risk Factors (Weighted)

Factor	Weight	High Risk Indicators
Defensible space	20%	Non-compliant zones
Vegetation density	20%	NDVI >0.6, high fuel load
Slope	15%	>30% grade
Roof material	10%	Wood shake, Class C
Structure spacing	10%	<30ft between buildings
Access/egress	10%	Single road, narrow

3DGS Quality Settings

Quality Level	Iterations	Time	Use Case
Preview	7K	5 min	Quick check
Standard	30K	30 min	General use
High	50K	60 min	Documentation
Inspection	100K	3 hrs	Damage measurement

Reference Files

Detailed implementations in references/:

• fire-detection.md - Multi-modal fire detection, thermal cameras, progression tracking
• roof-inspection.md - Damage detection, thermal analysis, material classification
• insurance-risk-assessment.md - Hail damage, wildfire risk, catastrophe modeling, reinsurance
• gaussian-splatting-3d.md - COLMAP pipeline, 3DGS training, inspection measurements

Integration Points

• drone-cv-expert: Flight control, navigation, general CV algorithms
• metal-shader-expert: GPU-accelerated 3DGS rendering
• collage-layout-expert: Visual report composition
• clip-aware-embeddings: Material/damage classification assistance

Insurance Workflow

1. Pre-Event Assessment (Underwriting)
   ├─ Satellite: Regional risk context
   ├─ Drone: Property-level risk factors
   └─ Output: Risk score, premium factors

2. Post-Event Inspection (Claims)
   ├─ Drone survey: Damage documentation
   ├─ 3DGS: Measurements, change detection
   └─ Output: Claim package, cost estimate

3. Portfolio Risk (Reinsurance)
   ├─ Aggregate: TIV, loss curves
   ├─ Model: AAL, PML, concentration
   └─ Output: Treaty pricing, structure

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Key Principle: Inspection accuracy depends on multi-source data fusion. Single-sensor assessments miss critical context. Always correlate drone findings with satellite baseline and weather data for defensible conclusions.