[pdal] Best practice for classifying ground on large cloud?

[Andrew Cunliffe]

Does anyone have recommendations for best practice for classifying ground
points on large clouds?

I'd like to use SMRF to classify ground points in a large cloud (ca. 920
000 000 points), but am exceeding the system memory (128GB).

It seems logical to split a large cloud into buffered tiles (to minimise
edge effects), then run a classifier on each tile, delete the buffer and
merge the tiles together again. It seems like this should be possible with
filters.splitter, but is this the right way to go?

Any suggestions on implementation much appreciated!
Regards,
Andy
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