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Fast and accurate point cloud classification.


Flai excels in point cloud classification with pretrained AI models spanning 19 categories for general mapping, 6 for forestry, and 21 for mobile mapping. In-house retraining ensures adaptability, especially in identifying challenging non-ground classes, reducing manual cleanup efforts. 

You have the option to choose from four distinct pre-trained classification models:

  • Aerial Mapping AI model,

  • Forestry AI Model

  • Mobile Mapping AI Model

  • Indoor Stockpile AI Model

  • Thin Ground AI model

Depending on the selected Aerial Mapping AI model, the following categories are available:

  • Other (All man-made objects not part of other categories)

  • Ground

  • Vegetation

  • Buildings

  • Low isolated noise

  • Water

  • Wires (low voltage, high voltage)

  • Powerline towers (low voltage, high voltage)

  • Railroad wires and towers

  • Bridges

  • High isolated noise

  • Roof objects (chimneys, antennas, solar panels)

  • Vehicles

  • Walls / Facades

  • Low points

  • Fences

The following classes can be extracted using the forestry AI model:

  • Ground

  • Vegetation

  • Tree trunks

  • Fallen trees

Mobile Mapping AI Model output the following categories:

  • Other

  • Roads

  • Sidewalks

  • OtherGround

  • Traffic Islands

  • Buildings

  • Trees and

  • Traffic lights nd Traffic signs

  • Masts

  • Wires

  • Pedestrian

  • Mobial and Stationary Vehicles

  • Noise

And two classes ouput for Indoor stockpile AI model:

  • Stockpile

  • Other (All man-made object not part of other categories)

The Thin Ground AI model can be utilized to refine the ground classified from any AI model. The model let you specify the output of the retaing Ground and filetred other points.

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