AI for 3D Point Clouds Processing: Application to Powerline corridors
Abderrazzaq Kharroubi
Researcher & Engineer in Geomatics
AI-based semantic segmentation of airborne LiDAR point clouds.
This training covers the full workflow for automatically classifying LiDAR point clouds of power line corridors: data preparation, feature engineering, classical ML, deep learning with RandLA-Net, and post-processing into delivery-ready LAS files with vegetation clearance distances. Each session combines slides with hands-on Python notebooks on real corridor data.
What you’ll learn
Classify airborne LiDAR point clouds of power line corridors using machine learning and deep learning.
Prepare point clouds for ML/DL: spatial indexing, ground filtering, downsampling, tiling
Compute geometric and height features that separate corridor classes.
Train and evaluate Random Forest and RandLA-Net classifiers on real corridor data
Post-process predictions and compute vegetation-to-wire clearance distances.
Learn directly from Abderrazzaq
Abderrazzaq Kharroubi
PhD in geomatics, contributor to research projects on 3D point clouds.
Who this course is for
You’ve tried learning on your own, but feel lost or stuck with tools like CloudCompare, QGIS, or Python.
You want to work smarter, save time, and deliver high-quality 3D results with confidence.
What's included
Live sessions
Learn directly from Abderrazzaq Kharroubi in a real-time, interactive format.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
Maven Guarantee
Your purchase is backed by the Maven Guarantee.
Course syllabus
10 live sessions • 13 lessons
Week 1
Jun
2
Live 1.1
Tue 6/25:00 PM—7:00 PM (UTC)Jun
4
Live 1.2
Thu 6/45:00 PM—6:30 PM (UTC)Jun
6
Live 1.3
Sat 6/69:30 AM—11:00 AM (UTC)
LiDAR technology and 3D point clouds
Point cloud processing fundamentals
Data preparation and annotation
Week 2
Jun
9
Live 2.1
Tue 6/95:00 PM—6:30 PM (UTC)Jun
11
Live 2.2
Thu 6/115:00 PM—6:30 PM (UTC)Jun
13
Live 2.3
Sat 6/139:30 AM—11:00 AM (UTC)
Feature engineering
Classical ML classifiers
Evaluation and error analysis
Schedule
Live sessions
16-18 hrs
4 weeks
Tue, Jun 2
5:00 PM—7:00 PM (UTC)
Thu, Jun 4
5:00 PM—6:30 PM (UTC)
Sat, Jun 6
9:30 AM—11:00 AM (UTC)
Frequently asked questions
€497
EUR