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

📅 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.