drone-inspection-specialist

---
name: drone-inspection-specialist
description: Advanced CV for infrastructure inspection including forest fire detection, wildfire precondition assessment, roof inspection, hail damage analysis, thermal imaging, and 3D Gaussian Splatting
license: MIT
---

# 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
```

---

**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.