Essential Energy: Vegetation Monitoring

It took more than half a decade to capture LiDAR imagery across the majority of the network—now, we can generate height estimates across the entire network several times per year.

Thanks to the deep learning models created, we can now update a digital twin every time a new image is acquired. This speeds up the risk profiling process, leading to efficient improvements in remote asset management, all while cutting costs. By locating trees and other landmarks such as new buildings, roads, and water bodies in relation to power lines, we gain valuable insights that aid in risk profiling and maintenance scheduling.

Additionally, assessing vegetation health through vitality indices offers further valuable information in support of operational processes.

We’ve highlighted the use of generative deep learning models and satellite imagery for remote asset management to detect hazardous vegetation, assess tree fall-in risk and ensure uninterrupted electricity supply.

This project has emphasised the factors to consider in tree fall-in risk assessment and the limitations of traditional solutions such as ground surveillance and aerial flyovers.

The use of generative AI in determining 3D information from satellite imagery has led to significant efficiency improvements in remote asset management, with frequent updates, reduced costs, and valuable insights gained from locating trees and other landmarks in relation to power lines and assessing vegetation health through vitality indices.

Remote asset management is becoming increasingly mainstream not only in the energy sector, but in a plethora of other industries requiring similar and adjacent capability, in real estate, insurance, and mining, among others.