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 reconstruction. Expert in multi-modal detection, insurance risk modeling, and reinsurance data pipelines. Activate on "fire detection", "wildfire risk", "roof inspection", "hail damage", "thermal analysis", "Gaussian Splatting", "3DGS", "insurance inspection", "defensible space", "property assessment", "catastrophe modeling", "NDVI", "fuel load". NOT for general drone flight control, SLAM, path planning, or sensor fusion (use drone-cv-expert), GPU shader development (use metal-shader-expert), or generic object detection without inspection context (use clip-aware-embeddings).
allowed-tools: Read,Write,Edit,Bash(python:,pip:),Grep,Glob,mcp__firecrawl__firecrawl_search,WebFetch,mcp__stability-ai__stability-ai-generate-image
category: AI & Machine Learning
tags:
- inspection
- fire-detection
- thermal
- gaussian-splatting
- insurance
pairs-with:
- skill: drone-cv-expert
reason: Core drone navigation and CV
- skill: clip-aware-embeddings
reason: Semantic understanding of inspected areas

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

Roof Damage Severity

Wildfire Risk Factors (Weighted)

3DGS Quality Settings

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.