[GRASS-dev] ] LiDAR LAS import - filter on import?

Fri Jun 3 09:30:03 EDT 2011

Markus,
        Were you planning on adding filtering options by return number on 
v.in.lidar?  It occurs to me that you could speed things up by only 
processing the subset of the data that you want to use.   I could see it 
being a useful thing to create a vector layer composed only of 1st returns 
( top of canopy/buildings)  Selecting only last returns is useful, as well 
as selecting points that are neither first nor last returns . 

        I've been primarily working with aggregate las files that I have 
dumped to text ( via liblas las2txt )  and then parse via python for 
first, middle, and last  returns. I have not taken the time to learn C 
yet, but perhaps the logic of these simple python programs would be useful 
.

The following is the python script I use to separate the last returns.
---------------------------------------------------------------------------------------------------------------------------------
import struct,os,string,re,binascii,glob
infile = raw_input("Enter the aggregate lidar filename:  ") 
outfil = raw_input("Enter the ASCII output filename for the Lidar Data: ")
intxt=open(infile,'r')
outtxt=open(outfil,'w')
while 1:
    lasline=intxt.readline()
    lasinfo=lasline.split(',')
    if (len(lasinfo))< 5:break
    numreturns=int(lasinfo[4])
    returnnum=int(lasinfo[5])
    # In the data input file for this instance, the number of returns
    # is the fifth column, and the return number is the sixth column ( 
x=1,y=2,z=3,intensity=4).
    # If the value of these colums is equal, it should be the last return.
    if ( numreturns==returnnum):
        outtxt.write(lasline)

intxt.close()
outtxt.close()
--------------------------------------------------------------------------------------------------------------------------------
For parsing the first returns,  substitute if (returnnum==1):

Here is the python script I use to separate out the "middle" returns.

-------------------------------------------------------------------------------------------------------------------------------

import struct,os,string,re,binascii,glob
infile = raw_input("Enter the aggregate lidar filename:  ") 
outfil = raw_input("Enter the ASCII output filename for the Lidar Data: ")
intxt=open(infile,'r')
outtxt=open(outfil,'w')
while 1:
    lasline=intxt.readline()
    lasinfo=lasline.split(',')
    if (len(lasinfo))< 5:break
    numreturns=int(lasinfo[4])
    returnnum=int(lasinfo[5])
    # In the data input file for this instance, the number of returns
    # is the fifth column, and the return number is the sixth column ( 
x=1,y=2,z=3,intensity=4).
    # If the value of these colums is equal, it should be the last return, 
so skip that entry
    # If the return number is 1 , skip that value.  All other values are 
middle canopy values,which is what we want.
    if ( numreturns==returnnum): continue
    if (returnnum==1):continue
    outtxt.write(lasline)
intxt.close()
outtxt.close()
------------------------------------------------------------------------------------------------------------------------------

I really appreciate your adding lidar data classifications from the 
standard into the program.  I haven't actually seen any lidar data with 
classifications yet, but I have hope for the future :-). 

As a bit of background , I'm taking the last returns and then running 
r.in.xyz to create a raster with the intensities as the z values to get 
relative soil moistures, and looking at points that are neither first nor 
last returns as a possible measure of vegetation density.  It works great 
for large datasets( > 4 billion points) , but I'm feeling the need for 
more point analysis.

Doug

Doug Newcomb 
USFWS
Raleigh, NC
919-856-4520 ext. 14 doug_newcomb at fws.gov
---------------------------------------------------------------------------------------------------------
The opinions I express are my own and are not representative of the 
official policy of the U.S.Fish and Wildlife Service or Dept. of the 
Interior.   Life is too short for undocumented, proprietary data formats.

Markus Metz <markus.metz.giswork at googlemail.com> 
Sent by: grass-dev-bounces at lists.osgeo.org
06/03/2011 02:20 AM

To
Hamish <hamish_b at yahoo.com>
cc
GRASS developers list <grass-dev at lists.osgeo.org>
Subject
[GRASS-dev] Re: [GRASS-user] LiDAR LAS import

Hamish wrote:
> Markus Metz wrote:
[snip]
>
>> v.in.lidar is a notch faster than las2txt | v.in.ascii. And
>> easier to use...
>
> I'm not too surprised the speed difference is not so huge, as
> unix pipes are very efficient. but the easier to use thing is
> very important.. both las2txt and v.in.ascii are a bundle of
> command line switches to get right.
>
>
>> Speed comparisons:
>>
>> # sample las file with 1,287,775 points
>>
>> # with table and topology
> ...
>> real    6m34.430s
> ...
>> real    6m13.823s
> ...
>> # without table, with topology
> ...
>> real    1m53.578s
> ...
>> real    1m44.876s
>
>
> I take it that without topology it runs in just seconds?

Update: no attribute table, no topology

time las2txt -i points.las --stdout --parse xyz --delimiter "|" |
v.in.ascii in=- out=points_ascii -ztb x=1 y=2 z=3

real             0m20.932s
user             0m18.424s
sys              0m6.869s

time v.in.lidar in=points.las out=points_las -obt

real             0m9.117s
user             0m2.946s
sys              0m5.985s

Markus M
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