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Explore Flai's recent innovations in automated LiDAR point cloud classification and processing through real-world use cases.

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Evaluation of FLAI AI Classification on LiDAR Data Collected with the AISPECO Heliux LITE System and RIEGL VQ-580 II-S

AISPECO and FLAI benchmarked AI point-cloud classification on airborne LiDAR captured with the HELIUX LITE platform and a RIEGL VQ-580 II-S. Four scenes—rural, dense urban, industrial, and powerline corridors—were flown at 540 m and 810 m AGL. Results showed point density and strip geometry strongly influence label quality: higher density improves building/vegetation separation, while lower density challenges small utility features like wires.

Jan 2013 min read

How to Keep Original (Ground) Labels Unchanged When Using Flai Classification

When you already have valuable labels in your point cloud data, you should not have to lose them when you run a new classification. This guide shows how to create a processing flow that keeps one or more input classes while applying Flai classification to everything else. A common pattern is to keep Ground from the input data while applying Flai’s classification to above-ground points. How Keeping Original Classification Works Under the hood, Flai’s AI models classify al

Nov 10, 20254 min read