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.