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Utility Corridor Management using Machine Learning

At Data Tapestry, our team's expertise spans a variety of specialties. While we've been able to apply NLP techniques, forecasting, and predictive analytics to many problems, most recently our team had to work with image data and the complexities that it presents. We combined resources with unmanned imaging experts at Skytec, LLC to create a solution for overgrowth and vegetation management in utility corridors. 

Damages in these areas due to overgrowth can occur without warning. Tower damage and power outages can cost millions of dollars in repairs and regulatory fines. It is even more important to detect these encroachments since an electricity arc or flashover can occur within less than 15 feet of power lines, thereby damaging equipment or causing fire to nearby vegetation. Unfortunately, manual efforts to monitor overgrowth can be extremely manpower intensive, expensive, and inefficient.

Our Solution

Imaging experts at Skytec provide aerial photos of utility corridors via unmanned and manned aircraft systems. Additional enhancement layers are added to the image to improve the visibility of the landscape and other objects. Next, our team will pre-process the images so towers, trees, roads, and buildings can be easily distinguished. Once the images have been processed, we train a multi-label classifier on labeled input images over several iterations. After that, we apply functions to calculate the difference in vegetation indexes over time. Then we execute an algorithm for calculating the distance between towers and other objects over time to determine if action needs to be taken. The result is the identification of vegetation and other objects within the corridor that could be potentially hazardous. 

If you are interested in learning more or seeing how this solution can be applied in your business, email us at business@datatapestry.ai or visit our website


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