AI Agentic Workflow for Satellite-Based Vegetation Encroachment Detection

The operational bottleneck is manual, periodic line patrols that are costly, inconsistent, and reactive. A custom workflow automates this by ingesting scheduled satellite feeds (e.g., Planet, Sentinel-2) into a geospatial data lake. Computer vision agents, orchestrated via LangGraph, analyze NDVI indices and spectral bands to detect encroaching vegetation, calculate its proximity to conductors, and assign a risk score based on species growth rate and local fire indices. This replaces weeks of field survey labor with daily, programmatic risk assessment.

Implementation integrates this detection pipeline with vegetation management software like Oracle Utilities or ServiceNow. The CMMS Integration Agent automatically generates pre-populated work orders with GPS coordinates, recommended actions, and compliance flags. Critical to rollout is a human-in-the-loop approval gate for high-cost items or environmentally sensitive zones, plus observability dashboards tracking false-positive rates and model drift. The architecture delivers ROI through reduced patrol costs, optimized crew schedules, and mitigated wildfire liability.

This workflow automates the manual, error-prone process of correlating satellite imagery with asset registers and maintenance schedules. It eliminates the lag between observable vegetation growth and scheduled trimming, directly reducing wildfire risk and manual survey costs. The operational upside comes from converting geospatial intelligence into prioritized, executable field actions within hours, not weeks, improving crew utilization and regulatory compliance.

Implementation integrates multi-spectral image pipelines with a LangGraph-based orchestrator managing specialized agents for detection, risk calculation, and system integration. Critical controls include confidence scoring for auto-approval, exception routing for ambiguous cases to human reviewers in ServiceNow, and full audit trails linking satellite tiles to final work orders. Deployment requires staging on cloud infrastructure with rollback capabilities for model updates.

Phase 1 establishes the core data pipeline and risk-scoring engine. We implement a cloud-based ingestion layer for multi-spectral satellite feeds (e.g., Sentinel-2, Planet), using computer vision agents to detect vegetation and calculate proximity to assets within a geospatial data lake. This initial phase delivers a high-fidelity risk map and validates the model's accuracy against historical inspection data, proving the detection logic's value before integrating with mission-critical systems. The focus is on measurable reduction in manual survey hours and false positive rates.

Phase 2 integrates the validated risk scores with your vegetation management software (e.g., SAP EAM, Oracle Utilities, or a specialized VM platform) via a secure API orchestration layer built with LangGraph. This phase introduces a human-in-the-loop approval gate for high-risk or high-cost work orders, ensuring operational control. The final phase expands autonomy, fusing drone LiDAR for validation and enabling fully automated, condition-based work order generation and crew dispatch. Each stage includes defined observability metrics—like processed acres per dollar and avoided PSPS events—to quantify the operational upside.

Effective governance starts with a multi-stage approval chain for high-risk alerts before any work order is dispatched. The orchestrator must route detections exceeding a calibrated confidence threshold to a human-in-the-loop review queue within the vegetation management platform (e.g., Epoch, Alden). This ensures field crews are not dispatched based on unvalidated AI signals, preventing wasted resources and maintaining operational trust. Approval gates are also required for any model retraining or logic changes to the risk-scoring agents.

Phased rollout is critical. Begin with a pilot corridor, running the AI workflow in parallel with manual surveys to benchmark accuracy and calibrate confidence thresholds. Phase two integrates the orchestrator with the CMMS (e.g., SAP, Maximo) for automated ticket creation but retains mandatory human approval. The final phase enables full automation for high-confidence, low-consequence alerts, while maintaining escalation paths. Continuous monitoring of false positive/negative rates and regulatory audit trails is non-negotiable for sustained operation.