What LiDAR inputs can be classified?
Airborne and drone LiDAR point clouds can be processed when the data includes sufficient density, coordinate reference information, and project coverage boundaries.
Solutions
LiDAR Classification and Intelligence
Scalable point cloud classification and feature extraction for airborne and drone LiDAR data.
We convert raw LiDAR point clouds into classified, structured geospatial outputs ready for mapping, spatial analysis, and downstream operational use.
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Point Cloud Pipeline
From dense LiDAR captures to organized intelligence layers.
Capabilities
Semantic segmentation of point clouds
USGS-standard classification
Utility-specific classification schemes
Individual tree extraction
Building footprint derivation from LiDAR
Derivative outputs for downstream GIS use
Typical deliverables
Classified point clouds
Semantic class layers
Utility-oriented classes
Individual tree points
Building footprint polygons
Terrain and surface derivatives
QC indicators and review layers
LIDAR Intelligence FAQ
Which classification schemas are supported?
Deliverables can follow USGS-standard classes or a utility-specific schema with vegetation, buildings, ground, bridge, wire, and review-focused classes.
What does the client receive?
Outputs are structured for GIS and production review, including classified point clouds, semantic layers, derivative surfaces, QC flags, and review layers.
How is quality control handled?
Automated classification is paired with QC indicators so production teams can target review time where the data is most likely to need inspection.
LIDAR Intelligence
Structured outputs for teams that need reliable geospatial production, not one-off demos.