This is a small example project for the 3D Powerline Vegetation Risk Analysis video.

Idea

Being able to detect Risk areas around power lines in an automated way is a game changer for energy network operators. This Rule Set provides a solution to extract these Risk areas in an automated, accurate and less time consuming way than traditional manual assessment. The idea of this project was to develop a rule set which classifies points of a point cloud into different Risk areas based on the 3D distance to a power line. The whole workflow within eCognition is automated, meaning you only need a point cloud as input and the Rule Set takes care of creating the deliverables, in this case a classified point cloud with Risk classes and the power line detected.

Download Data and Tutorial

Download eCognition Trial version here

Input Data

Unclassified Lidar *.las Point cloud

Output

Classified point cloud into:

• Powerline
• Ground
• Vegetation
  • Risk01 - Pro-Active Fault Clearance - PC#16
  • Risk02 - Branch Removal - PC#17
  • Risk03 - Underlying Vegetation - PC#18
  • Risk04 - Neighboring Forest - PC#19
• Other vegetation

Interesting related content:

• Tutorial - Working With LiDAR (point cloud) Files
• Rasterize Point Cloud
• OBIA Point Cloud Analytics