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Energy verified Verified Outcome TRL 9

Satellite Vegetation Management

domain Client: Large US Utility handshake Provider: AiDash schedule Deploy: 2020-Present
93 Impact
Enterprise Ready
Evidence Score: 5/10
Strength: High

Executive Summary

ANALYST: COI RESEARCH

Use of high-resolution satellite imagery and AI to predict tree growth along thousands of miles of transmission lines, optimizing trim cycles to prevent wildfires and outages.

rate_review Analyst Verdict

"Replaces 'cycle-based' maintenance with 'condition-based'. Vegetation contact is the #1 cause of outages and wildfires. Satellites scan the entire grid instantly, whereas ground patrols take years. The ROI in risk reduction is massive."

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Full Audit Report Available Includes Risk Register, Technical Specs & Compliance Data.

warning The Challenge

Utilities manage vegetation across vast, remote territories. Traditional methods involved walking the lines or flying helicopters/drones, which is expensive, slow, and provides only a snapshot. Fast-growing trees could hit lines between inspection cycles, sparking catastrophic wildfires (a major liability for PG&E).

psychology The Solution

Deployed AiDash's satellite analytics. The system ingests multispectral imagery from multiple constellations. The AI identifies tree species, measures distance to the wire, and predicts growth rates based on weather/soil. It generates a prioritized 'Trim Plan' focusing budget on the highest-risk spans rather than just trimming geographically.

settings_suggest Technical & Deployment Specs

Integrations
Work Order Systems
Deployment Model
SaaS
Data Classification
Geospatial
Estimated TCO / ROI
Subscription
POC Summary ( to )

"N/A"

shield Risk Register & Mitigation

Risk Factor Severity Mitigation Strategy
Cloud Cover Low SAR (Radar) data integration.
Resolution Low 50cm resolution imagery.

trending_up Impact Trajectory

Audited value realization curve

20% reduction in vegetation management budget Verified Outcome
Primary KPI15% improvement in grid reliability
Audit CycleElimination of blind spots in remote areas

policy Compliance & Gov

  • Standards: CPUC Fire Safety
  • Maturity (TRL): 9
  • Evidence Score: 5/10
  • Data Class: Geospatial

folder_shared Verified Assets

description
Verified Case Study
PDF • Version 1
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Technical Audit
PDF • Audited
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Security Architecture

The "Blind Verification" Protocol

How we verified these outcomes for Large US Utility without exposing sensitive IP or identities.

Private
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1. Raw Evidence

Audit ID: #PRIV-689
Evidence: Direct SQL Logs
Public
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2. Verified Asset

Outcome: Verified
Ref ID: #COI-689

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