Active Learning for 3D Data Labelling: 9-Step Workflow
I share the significance of labeling data, particularly in the context of 3D datasets for applications like geospatial analysis, autonomous driving, and robotics. I propose strategies to streamline the process, such as utilizing unsupervised learning approaches, active learning methods, and iterative model training. I outline a nine-stage process for active learning, where a small portion of the data is initially labeled manually, and the model is trained and iteratively refined with human feedback on low-confidence predictions.
By leveraging active learning techniques, companies can significantly reduce the time and resources required for data labeling while achieving high-quality results in model performance.
🍇 RESOURCES
3D Point Cloud Feature Extraction with Python:
Part 1: • 3D Point Cloud Feature Extraction Tutorial...
Part 2: • How to Build a 3D Interactive App in Pytho...
3D Tutorial Series: https://learngeodata.eu/3d-tutorials/
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Github: https://github.com/florentPoux
Research: https://scholar.google.com/citations?...
WHO AM I?
If we haven’t yet before - Hey 👋 I’m Florent, a professor-turned-entrepreneur, and I’ve somehow become one of the most-followed 3D expert. Through my videos here on this channel and my writing, I share evidence-based strategies and tools to help you be better coders and 3D innovators.
CHAPTERS 🌱
[00:00:00]: Importance of labeling data
[00:01:16]: Strategies for labeling 3D data sets
[00:02:25]: Introduction to active learning
[00:03:31]: Nine-stage process of active learning
[00:04:46]: Generating prediction scores
[00:06:05]: Selecting low-confidence data for labeling
[00:07:17]: Iterating the active learning process
[00:08:33]: Considerations for generating confidence measures
[00:09:39]: Encouragement to leverage active learning