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 trackingroof-inspection.md - Damage detection, thermal analysis, material classificationinsurance-risk-assessment.md - Hail damage, wildfire risk, catastrophe modeling, reinsurancegaussian-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.
Converted and distributed by TomeVault — claim your Tome and manage your conversions.
1---2name: drone-inspection-specialist3description: Advanced CV for infrastructure inspection including forest fire detection, wildfire precondition assessment, roof inspection, hail damage analysis, thermal imaging, and 3D Gaussian Splatting Use when this capability is needed.4---56# Drone Inspection Specialist78Expert in drone-based infrastructure inspection with computer vision, thermal analysis, and 3D reconstruction for insurance, property assessment, and environmental monitoring.910## Decision Tree: When to Use This Skill1112```13User mentions drones/UAV?14├─ YES → Is it about inspection or assessment of something?15│ ├─ Fire detection, smoke, thermal hotspots → THIS SKILL16│ ├─ Roof damage, hail, shingles → THIS SKILL17│ ├─ Property/insurance assessment → THIS SKILL18│ ├─ 3D reconstruction for measurement → THIS SKILL19│ ├─ Wildfire risk, defensible space → THIS SKILL20│ └─ NO (flight control, navigation, general CV) → drone-cv-expert21└─ NO → Is it about fire/roof/property assessment without drones?22 ├─ YES → Still use THIS SKILL (methods apply)23 └─ NO → Different skill needed24```2526## Core Competencies2728### Fire Detection & Wildfire Risk29- **Multi-Modal Detection**: RGB smoke + thermal hotspot fusion30- **Precondition Assessment**: NDVI, fuel load, vegetation density31- **Defensible Space**: CAL FIRE/NFPA 1144 compliance evaluation32- **Progression Tracking**: Spread rate, direction prediction3334### Roof & Structural Inspection35- **Damage Detection**: Cracks, missing shingles, wear, ponding36- **Hail Analysis**: Impact pattern recognition, size estimation37- **Thermal Analysis**: Moisture detection, insulation gaps, HVAC leaks38- **Material Classification**: Asphalt, metal, tile, slate identification3940### 3D Reconstruction (Gaussian Splatting)41- **Pipeline**: Video → COLMAP SfM → 3DGS training → Web viewer42- **Measurements**: Roof area, damage dimensions, property bounds43- **Change Detection**: Before/after comparison for claims4445### Insurance & Reinsurance46- **Claim Packaging**: Documentation meeting industry standards47- **Risk Modeling**: Catastrophe models, loss distributions48- **Precondition Data**: Satellite + drone + ground integration4950## Anti-Patterns to Avoid5152### 1. "Single-Sensor Dependence"53**Wrong**: Using only RGB for fire detection.54**Right**: Multi-modal fusion (RGB + thermal) for high-confidence alerts.55| Detection Source | Confidence | Action |56|------------------|------------|--------|57| Thermal fire only | 70% | Alert + verify |58| RGB smoke only | 60% | Alert + investigate |59| Thermal + RGB | 95% | Confirmed fire |6061### 2. "Ignoring Hail Pattern"62**Wrong**: Counting damage without analyzing spatial distribution.63**Right**: True hail damage has RANDOM distribution. Linear or clustered patterns indicate other causes (foot traffic, age).6465### 3. "Thermal Temperature Trust"66**Wrong**: Using raw thermal values without calibration.67**Right**: Account for:68- Emissivity of materials (roof = 0.9-0.95)69- Atmospheric transmission (humidity, distance)70- Reflected temperature from surroundings71- Time of day (thermal lag)7273### 4. "3DGS Frame Overload"74**Wrong**: Extracting every frame from drone video.75**Right**: Extract 2-3 fps with 80% overlap. More frames ≠ better reconstruction.76| Video FPS | Extract Rate | Result |77|-----------|--------------|--------|78| 30 | 30 (all) | Redundant, slow processing |79| 30 | 2-3 | Optimal quality/speed |80| 30 | 0.5 | Insufficient overlap |8182### 5. "Insurance Claim Speculation"83**Wrong**: Estimating costs without material identification.84**Right**: Identify material → Apply correct cost matrix.85| Material | Repair $/sqft | Replace $/sqft |86|----------|--------------|----------------|87| Asphalt shingle | $5-10 | $3-7 |88| Metal | $10-15 | $8-14 |89| Tile | $12-20 | $10-18 |90| Slate | $20-40 | $15-30 |9192### 6. "Defensible Space Zone Confusion"93**Wrong**: Treating all vegetation equally regardless of distance.94**Right**: CAL FIRE zones have different requirements:95| Zone | Distance | Requirement |96|------|----------|-------------|97| 0 | 0-5 ft | Ember-resistant (no combustibles) |98| 1 | 5-30 ft | Lean, clean, green (spaced trees) |99| 2 | 30-100 ft | Reduced fuel (selective thinning) |100101## Data Collection Strategy102103### Satellite Data (Regional Context)104- **Sentinel-2**: 10m resolution, NDVI, fuel moisture (SWIR bands)105- **Landsat-8**: 30m resolution, historical baseline, thermal band106- **Planet**: 3m resolution daily, change detection107- **Application**: Regional risk mapping, before/after events108109### Drone Data (Property Detail)110- **RGB Mapping**: 2-5cm GSD, orthomosaic, 3D model111- **Thermal Survey**: Moisture detection, heat signatures112- **Close Inspection**: Damage documentation, detail photos113- **Application**: Individual property assessment114115### Ground Truth116- **Slope Measurement**: GPS transects for topographic risk117- **Soil Sampling**: Moisture content for fire risk118- **Material Verification**: Confirm roof type119- **Application**: Calibration and validation120121## Quick Reference Tables122123### Fire Detection Confidence Levels124| Signal Combination | Confidence | Alert Priority |125|-------------------|------------|----------------|126| Thermal >150°C + Smoke | 95% | CRITICAL |127| Thermal fire model | 80% | HIGH |128| Hotspot >80°C | 70% | MEDIUM |129| Smoke only | 60% | MEDIUM |130| Hotspot 60-80°C | 50% | LOW |131132### Roof Damage Severity133| Type | Low | Medium | High | Critical |134|------|-----|--------|------|----------|135| Missing shingle | - | - | Always | - |136| Crack | <1" | 1-3" | >3" | Multiple |137| Granule loss | <10% | 10-30% | >30% | - |138| Ponding | - | Small | Large | Active leak |139140### Wildfire Risk Factors (Weighted)141| Factor | Weight | High Risk Indicators |142|--------|--------|---------------------|143| Defensible space | 20% | Non-compliant zones |144| Vegetation density | 20% | NDVI >0.6, high fuel load |145| Slope | 15% | >30% grade |146| Roof material | 10% | Wood shake, Class C |147| Structure spacing | 10% | <30ft between buildings |148| Access/egress | 10% | Single road, narrow |149150### 3DGS Quality Settings151| Quality Level | Iterations | Time | Use Case |152|---------------|------------|------|----------|153| Preview | 7K | 5 min | Quick check |154| Standard | 30K | 30 min | General use |155| High | 50K | 60 min | Documentation |156| Inspection | 100K | 3 hrs | Damage measurement |157158## Reference Files159160Detailed implementations in `references/`:161- `fire-detection.md` - Multi-modal fire detection, thermal cameras, progression tracking162- `roof-inspection.md` - Damage detection, thermal analysis, material classification163- `insurance-risk-assessment.md` - Hail damage, wildfire risk, catastrophe modeling, reinsurance164- `gaussian-splatting-3d.md` - COLMAP pipeline, 3DGS training, inspection measurements165166## Integration Points167168- **drone-cv-expert**: Flight control, navigation, general CV algorithms169- **metal-shader-expert**: GPU-accelerated 3DGS rendering170- **collage-layout-expert**: Visual report composition171- **clip-aware-embeddings**: Material/damage classification assistance172173## Insurance Workflow174175```1761. Pre-Event Assessment (Underwriting)177 ├─ Satellite: Regional risk context178 ├─ Drone: Property-level risk factors179 └─ Output: Risk score, premium factors1801812. Post-Event Inspection (Claims)182 ├─ Drone survey: Damage documentation183 ├─ 3DGS: Measurements, change detection184 └─ Output: Claim package, cost estimate1851863. Portfolio Risk (Reinsurance)187 ├─ Aggregate: TIV, loss curves188 ├─ Model: AAL, PML, concentration189 └─ Output: Treaty pricing, structure190```191192---193194**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.195196---197> Converted and distributed by [TomeVault](https://tomevault.io/claim/curiositech) — claim your Tome and manage your conversions.198<!-- tomevault:4.0:skill_md:2026-04-11 -->
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