Collaborated with one of the world's largest energy technology companies to deliver a state-of-the-art vegetation management system. Combined satellite imagery, LiDAR, and hyperspectral data with advanced AI models for unprecedented accuracy in tree detection, species identification, and risk prediction near critical infrastructure.
The Challenge
Managing vegetation near power lines across vast geographies is critical for preventing outages and wildfires. Traditional manual inspections are slow, expensive, and can't scale to continental coverage. The client needed an automated system that could detect, classify, and assess risk for millions of trees using remote sensing data.
Our Approach
We fused satellite imagery, LiDAR point clouds, and hyperspectral data into a unified analysis pipeline. Custom deep learning models handle tree detection, species identification, and growth prediction. A risk engine evaluates fall-in, grow-in, and overhang threats relative to infrastructure. The system runs on AWS SageMaker with QGIS-based visualization.
Results
The platform processes continental-scale data to identify and prioritize vegetation risks automatically. It replaced manual inspection workflows for one of the world's largest energy companies, reducing response times and improving accuracy across multiple risk categories. The partnership is ongoing.