Menu: AI
VisionLidar’s AI-powered tools bring automation and efficiency to LiDAR data processing, enabling advanced classification, segmentation, and model training. This section provides access to four AI modules designed to streamline workflows and improve accuracy.
AI Modules Overview
1. AI / Automatic Classification
🔹 Automatically classify LiDAR data using pre-trained or custom AI models.
🔹 Choose between GPU or CPU processing for performance optimization.
🔹 Supports corridor-based classification for linear projects.
📖 Learn more → [Automatic Classification Guide]
2. AI / Train Model
🔹 Train a new AI classification model using LiDAR point cloud data.
🔹 Choose between default profiles or custom training parameters.
🔹 Optimize training settings with validation splits, GPU acceleration, and performance modes.
📖 Learn more → [Train Model Guide]
3. AI / Export for Train
🔹 Prepare point cloud data for AI model training.
🔹 Two export options:
Fence Selection → Select part of a classified point cloud using a fence and export it as .PLY.
Full Export → Directly export the entire point cloud as .PLY for training.
🔹 Ensures high-quality training datasets for optimal AI model performance.
📖 Learn more → [Export for Train Guide]
4. AI / Classify LAS
🔹 Classify .LAS files directly without creating a project.
🔹 Select input/output folders, define the model, and choose the unit of measure.
🔹 Supports GPU acceleration and optimized performance modes.
📖 Learn more → [/wiki/spaces/VLEN/pages/3487072343]
Optimizing AI Performance
✅ GPU Acceleration: For best performance, use an NVIDIA GPU (recommended for large datasets).
✅ Performance Modes: Choose Minimal, Balanced, or High based on your system’s GPU memory.
✅ Data Preparation: Ensure high-quality, well-labeled training data for improved AI accuracy.
By leveraging these AI tools, you can enhance automation, reduce manual effort, and improve classification precision in your LiDAR workflows. 🚀