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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

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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.