Smithery is now a part of Arcade.dev! Read more in our announcement here
Advanced CV for infrastructure inspection including forest fire detection, wildfire precondition assessment, roof inspection, hail damage analysis, thermal imaging, and 3D Gaussian Splatting...
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?
āā YES ā Is it about inspection or assessment of something?
ā āā Fire detection, smoke, thermal hotspots ā THIS SKILL
ā āā Roof damage, hail, shingles ā THIS SKILL
ā āā Property/insurance assessment ā THIS SKILL
ā āā 3D reconstruction for measurement ā THIS SKILL
ā āā Wildfire risk, defensible space ā THIS SKILL
ā āā NO (flight control, navigation, general CV) ā drone-cv-expert
āā NO ā Is it about fire/roof/property assessment without drones?
āā YES ā Still use THIS SKILL (methods apply)
āā NO ā 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 ā 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 ā 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 >150Ā°C + Smoke | 95% | CRITICAL |
| Thermal fire model | 80% | HIGH |
| Hotspot >80Ā°C | 70% | MEDIUM |
| Smoke only | 60% | MEDIUM |
| Hotspot 60-80Ā°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)
āā Satellite: Regional risk context
āā Drone: Property-level risk factors
āā Output: Risk score, premium factors
2. Post-Event Inspection (Claims)
āā Drone survey: Damage documentation
āā 3DGS: Measurements, change detection
āā Output: Claim package, cost estimate
3. Portfolio Risk (Reinsurance)
āā Aggregate: TIV, loss curves
āā Model: AAL, PML, concentration
āā 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.
