[GRASS-user] Extracting building footprints from DSM/DTM/LiDAR point cloud

[LeeDaniel]

Hi there,
I'm working on a project to create a large amount of building footprints.
We're planning on giving the building footprints to OSM in the end anyway,
so we briefly considered using the Bing imagery in order to digitalize them
manually, but to save labor and set up a process that will also work welwell
in other areas it would be much better to minimize manual classification if
at all possible.  That's why we're trying to do the following:
1. Separate non-terrain objects from terrain
2. Separate buildings from other structures
3. Convert 2D building footprints to vectors anand smooth the boundaries

Does anyone have any experience with this kind of operation? I've tried a
few things and have a couple more ideas too:
- Produce the footprints using a normalized digital surface model. I
produced one by subtracting the digital surface model from the digital
terrain model (from the data provider) and then classified for all objects
over 2 m high. This kept most of the trees and part of our study area is
suburban, meaning that there were lots of trees. Most of them coule be
eliminated by converting the remaining pixels to areas and then deleting all
areas that didn't contain address points, but the results were still at best
disappointing.
- Another idea I have is to take the LiDAR raw data and try to extract the
trees from it, then producing a DSM from the remaining points and
normalizing it like above, this time definitely without trees. Since I've 
never worked with raw data from LiDAR scans before, I imagine this task as
being kind of daunting, but nonethless quite interesting. The omnipresent
time constraints mak me want to find a way around it, though I don't see
how.

Anyway, I was just wondering if anyone has any experience here that could
help me. If not I'll probably end up scripting an algorithm to separate the
trees by the echo they return, if anyone's interested in having it I'd be
happy to share. Thanks!

Best,
Daniel

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