On Dec 16, 2010, at 2:11 PM, Peter Tittmann wrote:
> Greetings all,
>
> I wanted to see if someone could advise as to the most efficient use of
> LibLAS for the following:
>
> I have a series of 200-500mb las files with classified points.
>
> I have a program (C++) that reads in .txt files and doesn't like them
> bigger than about 10mb. I have written some python to batch the C++ app for
> multiple tiles. Obviously, using LibLAS to load the .las files directly into
> the app would be best but due to time/resource constraints thats not going to
> happen.
I have just added the ability for lasblock to output .las files using the
--write-points option. Because of the way the chipper works, we can't filter
*and* chip the data at the same time, so you'll have to do either the filtering
or the chipping first, and then the other.
>
> I need to subset the large tiles spatially and by return #/classification. My
> idea at the moment is to use write a script to batch las2las2 to produce text
> files for each combination of spatial extent and point class, then use the
> python api to produce text files that are digestible by the app.
las2las2 is gone. It's all just las2las now. The previous incarnation is
called las2las-old, which retains the old calling conventions and arguments if
you need it.
>
> My question (finally), is weather this is the best way to approach this
> problem from an efficiency standpoint. I have used the python api to read
> through the points in the original (large) .las files and spit out text files
> with my criteria but its very brute force and slow.
Using the above new functionality:
lasblock my_500_mb_file.las --capacity 1000000 --write-points
million_point_file.las
#!/bin/bash
for i in $(ls million_point_file*.las)
las2las $i $i-filtered.las --my-filter-options
done
Hope this helps,
Howard_______________________________________________
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