Hi Jochen
we try to find high and low noise points (outliers, birds, incorrect reflections on roof windows….).
first we use lasheight to calculate the height above ground
Then we use several filters e.g. and set the classification of the filtered points to 0
las2las.exe -i *_1.laz
-drop_last_of_many
-keep_class 20
-keep_z 45 100
-keep_intensity_below 20
-set_classification 0
-odix _first20_between45-100
-olaz -cpu64 -cores 6
To check and edit the result (maybe there are some powerlines we want to keep in class 20) we convert the points to shape and edit the result in a GIS (this is, we remove the falsely classified points from the shape to keep only real the outliers. Tthis works nicely in a GIS, where we can see the powerline route e.g.)
Then we convert the shape back to las and with lascopy copy the classification (0) to the real outliers.
I hope, this is not too confusing.
Thanks
Karin
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Hi Jorge,
as a matter of fact, up to now we used the text-format. I wanted to reduce the steps and to shorten the workflow. Normally there are only few points that are misclassified.
We have QGIS and ArcMap. For you, QGIS should be ok.
And yes, we use lasnoise, too.
Thank you
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