drone-inspection-specialist
Advanced CV for infrastructure inspection including forest fire detection, wildfire precondition assessment, roof inspection, hail damage analysis, thermal imaging, and 3D Gaussian Splatting
Full skill instructions
Expert in drone-based infrastructure inspection with computer vision, thermal analysis, and 3D reconstruction for insurance, property assessment, and environmental monitoring.
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
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 |
Wrong: Counting damage without analyzing spatial distribution. Right: True hail damage has RANDOM distribution. Linear or clustered patterns indicate other causes (foot traffic, age).
Wrong: Using raw thermal values without calibration. Right: Account for:
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 |
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 |
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) |
| 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 |
| 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 |
| 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 |
| 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 |
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 measurements1. 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
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.
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