• Renewables
  • Webinar: Reducing Wildfire Risks And Grid Disturbances with AI
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Webinar: March 12, 2020

Webinar: Reducing Wildfire Risks And Grid Disturbances with AI

Event details

What

Webinar: Reducing Wildfire Risks

Where

GoToWebinar - 9AM Central

When

March 12, 2020

Webinar: Reducing Wildfire Risks And Grid Disturbances with AI Based Integrated Satellite & Drone Vegetation Inspection

For most power distribution utilities, vegetation management is one of the largest operating expenses. The current processes used to perform inspections and to schedule vegetation management cycles has not only proven to be very costly, but has also not been fully effective at reducing wildfire ignition risks.

In the GridEyeS project with the European Space Agency, StormGeo has been researching and developing a new satellite and drone technology approach to vegetation management that shows promise to significantly reduce inspection and vegetation management costs by well over $100 per mile of line per year while also reducing the risks of wildfire. GridEyeS uses AI to process low-cost government satellite images, commercial high-resolution satellite images, and drone imagery, so that each tree’s risk metadata such as distance from line, height, health, and growth rate, can be used to classify the risk levels for all of the vegetation along the grid.

Register for the webinar to learn how the GridEyeS technology will provide the following benefits:

  • Identify high fire-risk vegetation to ensure that vegetation maintenance resources are allocated to mitigate these risks first
  • Provide full inspection of the grid vegetation continually throughout the year
  • Measure performance of current vegetation maintenance programs and contractors
  • Reduce high cost inspection processes
  • Increase system reliability through reduced vegetation related outages

All those who register will directly receive a recording of the webinar. 

Register Now

Stormgeo key speakers
Reza Arghandeh

As a member of the Research and Development team, Reza is focused on developing machine learning algorithms to combine and analyze large scale data sets from weather, infrastructure networks and demographics. This helps StormGeo forecast the effects of extreme weather on urban infrastructure and improve emergency response and restoration—an essential application as climate change causes harsher weather.