Enview creates AI-powered to detect trees near power lines

Enview automatically detects grow-in and fall-in vegetation that could pose risk to power lines. Results are displayed on an intuitive and secure Web Portal.

 

Enview, a Silicon Valley-based data processing company, has launched a new utility, powered by artificial intelligence, to identify the exact location and clearances of high-risk vegetation early, and at scale, near power lines. The tool will help operators prioritise and address the problem areas.

Enview Founder & CEO Dr. San Gunawardana, said: “The scale of this challenge is staggering. It simply is not reasonable to expect the traditional manual processes used by many utilities to prevent these emergent threats. The results are catastrophic and we’re seeing that play out not only here in California, but also worldwide. New technology can help solve these challenges. It’s why we’ve designed and built a solution using the latest in artificial intelligence, machine vision, and cloud computing to enable accurate and extremely fast identification of at-risk spans.”

Enview is available as a software subscription. It automatically classifies and analyses the data to produce intuitive and accessible data visualisations that are field-ready, meaning that they can be delivered directly to the workforce in the field for immediate response.

“Ever since we completed our 2003 Northeast Blackout investigation, LiDAR-driven programmes have helped utilities identify threats, but LiDAR is currently constrained by taxing levels of manual data manipulation. As an industry, we have to look to new advancing technologies like AI that enable us to automatically see what we can’t see and to do so in days and weeks, not months,” said Stephen Cieslewicz, a nationally recognised expert in Utility Vegetation Management.

This is the kind of things that AI is best suited for. It is almost impossible for human beings to monitor tree growth near power lines in remote areas. The tool, if implemented correctly, will help prevent disasters.

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