Computer-Vision Drone Inspector for Transmission Lines & Wild-Fire Risk.

This workshop trains participants to use drone-based computer vision for inspecting transmission lines and assessing wildfire risks. It covers data capture, flight planning, model development for defect detection and vegetation encroachment, and risk mapping, enabling participants to generate actionable reports for improved safety and decision-making.

📅 Day 1 – Foundations & Data Capture

• Mission scope: defects, vegetation encroachment, wildfire ignition cues
• Platforms & payloads: multirotor vs fixed-wing; RGB/thermal/multispectral; radiometric & geo calibration
• Flight planning & compliance: geofencing, BVLOS limits, safety near energized lines
• Data & labels: imagery/video, orthos/point clouds; annotation schema & severity tags
• Hands-on: Plan a corridor mission (flight lines, camera settings) and produce a data-collection checklist

📅 Day 2 – Computer Vision Pipeline

• Preprocessing: sync/geotag, deblur/distortion, tiling, class balance
• Models: detection/segmentation for hardware & insulators; vegetation encroachment; thermal hotspot flags
• Robustness & evaluation: glare/motion/scale, active learning; precision/recall, per-class AP, uncertainty gates
• Hands-on: Fine-tune a compact detector+segmenter and export inference (boxes/masks) with a defect/encroachment summary

📅 Day 3 – Wildfire Risk & Operations

• Risk layers: fuel dryness, slope/aspect, weather/Red-Flag indices, proximity to lines, prior ignitions
• Fusion & workflow: combine CV outputs with terrain/weather; triage queue, human-in-the-loop, CMMS ticketing
• Deployment: edge vs cloud inference, bandwidth limits, encryption, model drift/rollback
• Hands-on: Build a corridor risk map and generate a one-page report with geotagged findings, risk ranking, and actions

• Professionals in electrical engineering and infrastructure maintenance

• Researchers in wildfire management and environmental monitoring

• Individuals working with drone technology, computer vision, or data analytics

• Those involved in transmission line inspection and vegetation management

• Professionals focused on wildfire risk assessment

• Basic knowledge of drones and computer vision recommended but not required

• Proficiency in drone-based data capture for transmission line inspections and wildfire risk assessment.

• Hands-on experience in developing and fine-tuning computer vision models for defect detection and vegetation encroachment.

• Ability to integrate computer vision outputs with environmental and wildfire risk layers.

• Skills to generate actionable risk maps and operator-ready reports.

• Enhanced understanding of flight planning, compliance, and safety protocols for drone operations.