Hey all 
Currently exploring the task of semi-automated point cloud classification (e.g. labeling points in a point cloud as belonging to one of several classes, e.g. ground, road, building, vegetation, etc.).
I’m having difficulties finding labeled point cloud datasets that could be used for training a classifier.
Does anyone know of good resources?
Found so far:
I’ve been thinking about training a neural network to do that but I have not got to it, mainly a time problem.
But I’m going to play a bit with R and hopefully get some value from that.
Take a look at ISPRS:
I haven’t used them myself, but they should include reference publications so you can figure out how well you are doing against the published state-of-the-art as well.
Edit, also these (some are 2D, some 3D, and a mix of LiDAR and photogrammetric):
Useful, thanks!
Also looking for suggestions on software for manually classifying point clouds. I know this can be done with CloudCompare, but perhaps there are more specialized tools.
I’m assuming that you want FOSS tooling, right?
I used to use Merrick MARS.
I have some LiDAR from my thesis processed in MARS still, and my municipality releases all their classified LiDAR from Pictometry as open data.
Preferably 
I imagine you could do it all with PDAL. They’ve recently added pdal-parallelizer, which should speed things up too.
PDAL has only ground/non-ground classifiers. Filters — pdal.io (which we already use).
I’m (roughly) following the method of:
Timo Hackel, Jan D Wegner, and Konrad Schindler. Fast semantic segmentation of 3d point clouds with strongly varying density. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Prague, Czech Republic, 3:177–184, 2016
Which is for the most part conveniently implemented in CGAL.
Sure, but there’s more options than that. You could use filter range to query RGB values, or height above ground values, or a std limit based off some clustering function for finding shapes, and you can tag those queries for classifying or probably set a new classification value (i havent tried this yet). Here’s me just messing around with different filters in a pipeline. This isn’t a polished example, but it tags the original PC, samples it for faster ground classification, then merges the classified PC to create a HAG which i then filter to the limits, -0.5m to 50m. I imagine with a few more lines of code you could tag vertical intervals based of some max z value and RGB value to stratify veg.
But your first attempt looks pretty great with that code!
It’s like CGAL Polyhedron if I understand well. It needs some enhancements and tweaks to perform better
That’s very nice!
I hope to get that working in R.
I would need a function that searches for specific features and give me a centroid point for each future.
It would really help me in my field of work
Oh, this is phenomenal!
Well a bit noisy; I think there’s lots of room for improvement. I’m going to replace random forests with gradient boosting and see how that performs, try various scales, plus I haven’t verified that all features are computed perfectly.
Very cool.
What classes are being used? LAS/ASPRS normal ones?
The addition of Eye Dome Lighting helps legibility a ton, too.
