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.

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 all available...

Urban trees are vital to the health and sustainability of city environments. They provide a range of ecosystem services, such as cooling...