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).
- "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
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Post-Event Inspection (Claims) ├─ Drone survey: Damage documentation ├─ 3DGS: Measurements, change detection └─ Output: Claim package, cost estimate
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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.