# Pattern: vegetation-management imagery

A regional electric utility operates an AI vegetation-management system that processes drone and helicopter imagery of power-line corridors. The model flags encroaching trees, structural damage, and clearance violations. Output drives field crews to inspect, trim, and remediate before a hazard turns into an outage or — in fire-prone regions — a wildfire ignition.

In summer, leafy canopy is easy to see against the lines. In winter — bare branches against bright sky, snow-occluded sightlines — model accuracy can drop by double digits. **Aggregate annual accuracy hides this**. Stratified evaluation surfaces it.

This pattern shows how to evaluate vegetation-management imagery models across seasonal and lighting conditions so blind spots are measured, not discovered.

## What's at stake

| Risk dimension                                          | Magnitude                                               | Framework                          |
| ------------------------------------------------------- | ------------------------------------------------------- | ---------------------------------- |
| Catastrophic-fire civil liability                       | $30B+ for the most-cited single utility liability event | Public CPUC and bankruptcy filings |
| NERC and state PUC enforcement on vegetation management | Per-finding civil penalty bands                         | Public NERC enforcement orders     |
| Per-hour outage cost from a downed line                 | $10K–$500K depending on customer mix                    | Industry reliability benchmarks    |
| Loss of life from wildfire ignition                     | Beyond financial accounting                             | Public wildfire-incident records   |

## The evaluation pattern

A **stratified evaluation** treats season, lighting condition, and equipment as first-class dimensions.

1. **Image-classification scorer** — per-class precision and recall on hazard categories (vegetation encroachment, equipment corrosion, clearance violation). Trace tags include `season`, `lighting_condition`, and `equipment_type`; results break out per-tag.
2. **Custom code grader (seasonal disparity)** — calculates hazard-detection-rate disparity between winter and summer trace sets; flags any disparity above a configured tolerance (e.g., 5%) as a regression.
3. **Compare models** — when a new model is proposed, it must close the seasonal disparity without losing summer performance. The comparison view shows side-by-side per-season scores.
4. **Continuous trace evaluation** — daily scoring on production drone-imagery samples. During high-risk seasons (fire season for fire-prone regions, winter ice for cold regions), cadence increases to hourly. Threshold alerts route to vegetation-management operations via Slack and email.

## Configuration in code

```python
# Python (SDK)
from layerlens import Stratix
client = Stratix()

seasonal_disparity = client.scorers.create_code(
 name="seasonal-disparity",
 code="""
winter_acc = mean(score for trace, score in zip(traces, scores) if trace.tags['season'] == 'winter')
summer_acc = mean(score for trace, score in zip(traces, scores) if trace.tags['season'] == 'summer')
result = {'passed': abs(winter_acc - summer_acc) <= 0.05, 'gap': summer_acc - winter_acc}
""",
)

trace_eval = client.trace_evaluations.create(
 trace_set={"tags": {"feature": "vegetation-management"}, "sample_rate": 0.01},
 scorers=[seasonal_disparity.id, hazard_classifier_scorer_id],
 schedule="daily",
)
```

```typescript
// TypeScript (REST)
const r = await fetch("https://stratix.layerlens.ai/api/v1/trace-evaluations", {
 method: "POST",
 headers: {
 "X-API-Key": process.env.LAYERLENS_STRATIX_API_KEY!,
 "Content-Type": "application/json",
 },
 body: JSON.stringify({
 trace_set: { tags: { feature: "vegetation-management" }, sample_rate: 0.01 },
 scorers: [seasonalDisparityId, hazardClassifierId],
 schedule: "daily",
 }),
});
```

## What you get

* Seasonal and lighting-condition disparities become measured dimensions on the dashboard, not undiscovered blind spots.
* Winter-specific training-data investment and evaluation-driven model improvements close the gap to within tolerance before the next high-risk season.
* Auditor-ready evaluation evidence for state PUC and NERC reviews — every release pin cites the evaluation IDs that gated it.
* Operations teams trust the model because the variance is explained, not absorbed into an opaque average.

## Stratix capabilities used

* [Custom code graders](https://github.com/LayerLens/gitbook-full/blob/main/08-evaluate/cookbook/custom-code-scorer.md) — seasonal-disparity computations
* [Compare models](/5.-select-pick-the-model/compare-models.md) — variant selection with seasonal-fairness criteria
* [Trace evaluations](/8.-evaluate-score-the-outputs/trace-evaluations.md) — daily continuous scoring during high-risk seasons
* [Notifications](https://github.com/LayerLens/gitbook-full/blob/main/13-reference/sdk-python/notifications.md) — Slack and email routing
* [Audit-friendly evidence guide](/11.-admin/audit-friendly-evidence.md)

## Replicate this

* [Industry → Energy and utilities](/4.2-industry-use-cases/energy-utilities.md)
* [Workflow: Govern](/9.-improve-tune-the-system/workflow.md)
* [Concept: Continuous evaluation](/7.-observe-see-whats-happening/continuous-evaluation.md)


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