Pattern: vegetation-management imagery

Energy / utilities — vegetation-management imagery pattern. Stratified accuracy by season, variant comparison, daily continuous evaluation in fire season.

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 (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 (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 — seasonal-disparity computations
• Compare models — variant selection with seasonal-fairness criteria
• Trace evaluations — daily continuous scoring during high-risk seasons
• Notifications — Slack and email routing
• Audit-friendly evidence guide

Replicate this

• Industry → Energy and utilities
• Workflow: Govern
• Concept: Continuous evaluation