How we combine machine learning classification with BSc+ engineer validation to process 500 km corridors in days, not weeks.
Classifying a raw LiDAR point cloud into ground, vegetation, and structure classes has traditionally been a slow, manual task: a skilled technician tracing tile by tile through millions of points. At corridor scale, that approach simply does not fit modern delivery timelines.
Our pipeline runs a machine-learning classifier as the first pass, separating the bulk of the point cloud into candidate classes in a fraction of the time a manual pass would take. The output is never shipped as-is: every tile is reviewed by a BSc+ geospatial engineer, who corrects misclassifications and validates edge cases the model handles poorly: dense canopy over structures, overlapping vegetation strata, and similar ambiguous returns.
The combination lets us process 500 km corridors in days rather than weeks, while keeping classification accuracy at a level clients can use directly in downstream engineering workflows, without a second QA pass on their end.
This is the same principle behind every production pipeline we run: automation for volume, qualified engineers for accuracy.
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