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.