Case Study

Netze BW realizes high accuracy with satellite and AI vegetation assessments


Netze BW realizes high accuracy with satellite and AI vegetation assessments

AiDash climate technology measures tree canopy location and health to advance Netze’s vegetation management


  • Company: Netze BW
  • Location: Germany
  • Solution: AiDash Intelligent Vegetation Management System (IVMS)


Reported high danger tree risk in 24 spans

Identified 153 spans with high grow-in risk

Detected medium danger tree risk in 86 spans



Determine effectiveness of remote sensing of vegetation hazards

Would data from satellite and AI remote sensing and analysis of medium voltage overhead power lines improve electric service levels for Netze BW GmbH?

As the largest grid company for electricity (as well as gas and water) in the German state of Baden-Württemberg, Netze BW GmbH keeps a constant lookout for improvement opportunities. The utility opted to consider how a satellite and AI solution might help ensure its end users receive reliable and cost-effective services, across the 93 stations and more than 17,521km2 (about 10,887 miles2) that it serves.

Netze teamed with AiDash to see what resolution, data, and recommendations AiDash Intelligent Vegetation Management System (IVMS) would deliver at 2 locations with differing geographies: ED Netze in Rheinfelden, and Netze ODR in Ellwagen (Jagst).

Specifically, Netze wanted to examine a corridor of at least 80 meters (87.5 yards):

  • Identify vegetation, vegetation type and growth prediction (for forest areas, single trees or small groups of trees).
  • Measure distance of vegetation to power lines and predict encroachment risk to power line segments.
  • Determine the type of remote sensing imagery best suited for this use case.



Take multiple scans with remote imagery to ID risk factors

AiDash configured and customized IVMS to ensure that Netze’s challenges could be addressed. IVMS would:

  • Identify vegetation, categorized as per proximity to the overhead line.
  • Determine criticality — high, medium, and low risk — based on multiple parameters, including horizontal and vertical clearances, hazard tree risk, tree growth, etc.).
  • Predict multidirectional growth rate at the segment level.
  • Provide actionable insights rather than volumes of data.
  • Ensure changes can be tracked over time.

Check accuracy of GIS files and make shapefile corrections as needed to ensure GIS data is fully aligned with the network on the ground.



Satellite and AI assessments correlate with ground truth

AiDash IVMS looked at vegetation in the Netze rights of way, identifying the tree canopy, measuring height. and calculating distance from the conductor using satellite remote sensing imagery.

Conductor spatial positions were modelled to consider:

  • Height of lowermost conductor.
  • Sag and sway of lines.
  • Distance between centerline and outermost wire.

Once IVMS was in place at Netze, within 3 weeks the system delivered results. Using its AI analysis and predictive capabilities, it determined that, within a group of 790 spans:

  • 153 (or 19%) had high grow-in risk, showing encroachment within 3 meters of radial distance from conductors.
  • 131 (or 17%) contained high pole risk factors, with vegetation encroaching within 1 meter of poles in Netze’s reviewed rights of way.

The solution homed in on hazard trees, identifying possible striking trees with multiple scans, and determining tree height and distance from the wire. IVMS also considered striking tree density, soil type, and history of repeated outages in the area, to assess hazard tree risk.

With these factors in mind the AiDash team was able to caution Netze to danger tree risk in spans within the examined rights of way. They determined within a group of 790 spans:

  • 24 (about 3%) were at high danger tree risk.
  • 86 (almost 11%) were at medium danger tree risk.

This precise detail would allow Netze to plan specific critical growth removal to avoid fall-in risk and possible outages.



About AiDash

AiDash is an AI-first vertical SaaS company on a mission to transform operations, maintenance, and sustainability in industries with geographically distributed assets by using satellites and AI at scale. With access to a continual, near real-time streams of critical data, utilities, energy, transportation, water and wastewater and other core industries can make more informed decisions and build optimized long-term plans, all while reducing costs, improving reliability, and achieving sustainability goals. To learn more about how AiDash is helping core industries become more resilient, efficient, and sustainable, visit

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