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 neededCore 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 trackingroof-inspection.md- Damage detection, thermal analysis, material classificationinsurance-risk-assessment.md- Hail damage, wildfire risk, catastrophe modeling, reinsurancegaussian-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, structureKey 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.