AI for LiDAR Point Cloud Classification
Abderrazzaq Kharroubi
PhD in Geomatics · LiDAR & AI Specialist
Build a production-grade AI pipeline for 3D point clouds.
A 2-week live cohort to build a production-grade AI pipeline for 3D LiDAR point clouds, end to end. You build the full workflow: data preparation, feature engineering, classical machine learning baseline, deep learning with LitePT, post-processing rules, and ASPRS-compliant LAS export. By week 4 you have a pipeline that runs on your own data and produces files clients can use.
The anchor application is power line corridor classification, the same workflow I built during my PhD on Belgian railway corridors. It transfers directly to forestry, urban mapping, mobile mapping, and rail.
What you’ll learn
Build a production-grade AI that classifies LiDAR corridors (or rails...) end-to-end and delivers ASPRS LAS files that clients can use.
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 LitePT 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
A surveying or geomatics engineer delivering classified LAS to utility, rail or telecom clients, and tired of doing it by hand.
A GIS or remote-sensing professional who wants to add AI-based classification to your service offering.
A researcher or PhD student working on point clouds and ready to move past tutorial notebooks to a real pipeline.
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.
Cohort community
Ask questions between sessions, share your results, get feedback.
Free signed copy of my upcoming book
Founding-cohort participants get a complimentary signed copy of my upcoming book on AI for 3D point clouds, shipping when published.
Maven Guarantee
Your purchase is backed by the Maven Guarantee.
Course syllabus
3 live sessions • 14 lessons
Week 1
Nov
3
Kick off
Tue 11/36:00 PM—7:30 PM (UTC)Nov
7
Follow up
Sat 11/712:00 PM—2:00 PM (UTC)
LiDAR technology and 3D point clouds
Point cloud processing fundamentals
Data preparation and annotation
Week 2
Nov
12
check classification results and updates
Thu 11/127:00 PM—9:00 PM (UTC)
Feature engineering
Classical ML classifiers
Evaluation and error analysis
Deep learning for point clouds: the architectures
Training a model, the complete pipeline
Going to production
Final Q&A
Schedule
Live sessions
4-6 hrs
Build a production-ready classifier in 2 weeks
Tue, Nov 3
6:00 PM—7:30 PM (UTC)
Sat, Nov 7
12:00 PM—2:00 PM (UTC)
Thu, Nov 12
7:00 PM—9:00 PM (UTC)
Let's begin
Frequently asked questions
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