Point cloud classification

Perform semantic segmentation (classification) of LiDAR point clouds using AI models. There are Advanced and Basic models available for each AI model. The categories you get in the end depend on your selection.

You can choose pre-trained AI models derived from semantic segmentation datasets that are continuously labelled by a team of experts in the geospatial and forestry domain.

Depending on the selected geospatial AI (geoAI) 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

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

• Ground

• Vegetation

• Tree trunks

• Fallen trees

If you notice areas where the AI model does not perform optimally, use Tools for manual annotation directly on Flai's cloud-based machine learning platform.

Annotation-as-a-service is available if you would like us to do data labelling and annotation services to improve the quality and applicability of the AI model.