Dear @Daniel and @Dimitri,
I've classified a very large photogrammetry based point cloud (approx. 280 Million points) using qCANUPO. I constructed my own classifier using trial and error in Train Classifier and I really take this opportunity to thank you guys for your work, especially because you've made it open source.
For flat to generally undulating areas, the results have been amazing I've extracted bare earth model, that is good enough for my project (Figure 1). Area looking black are actually tree shadows and not trees.
However, for relatively steep slopes with thick tree cover and/or surface erosion debris, there're certain points which were classified into vegetation but actually correspond to earth/ground, e.g. For one such area in the project site, post classification results are shown in Figure 3 (Bare Earth)
and Figure 4 (Vegetation).
In Figure 4 you can see that there points (white/brown) that should've been classified as bare-earth but weren't. Could you guys suggest what is the pblm here? The scale for my classifier varies from 0.10 to 25 with a step of 0.20. I've used upper limit to be 25 because there're certain houses on project site that are close to 25m in length.
Also, how can I save the classified point cloud so that I could use that classification later. I've saved the classified point cloud as .bin and .las/laz formats. .bin file would not open afterwards, and .las/laz file doesn't show classification (or scalar fields) once saved and reopened.
Is there a way to classify river/stream water as a separate entity? So far, it gets classified into vegetation. DO I need to train another classifier for that?