DLC
Applied Semantic Segmentation for LiDAR Point Cloud
Apply semantic segmentation to real LiDAR point clouds. Classify ground, buildings, vegetation, and objects with Python automation.
Note from Florent
Semantic segmentation is where LiDAR becomes truly useful. I’ve built this around the real-world classification workflows I run in production every week.
What you will build
Module 01
LiDAR semantic segmentation
Classify point clouds into semantic categories using automated Python pipelines.
Module 02
Production workflows
Deploy segmentation on large-scale LiDAR datasets. Batch processing and evaluation.
Your starting resources
Additional guides and code packs unlock as you progress through each module.
Course Content
3D Semantic Segmentation: Foreword
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🐍 3D MACHINE LEARNING
3D Machine Learning: Detection, Classification, Segmentation
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3D Machine Learning: Unsupervised Segmentation Fundamentals
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3D Point Cloud Unsupervised Labelling (K-Means)
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3D Machine Learning: Supervised Learning Workflow
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3D Point Cloud Supervised Learning: ML Solutions
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🐲 3D DEEP LEARNING
3D Deep Learning: Starting Folder 📦
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A First Deep Learning Python Setup
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PointNet Architecture: Deep Dive
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PointNet: Architecture Implementation
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PointNet Data Preparation: Part 1
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PointNet Data Preparation: Part 2
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PointNet: Model Creation and Training
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PointNet: Model Inference and Evaluation
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3D Deep Learning System Design Thinking
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