In blatant violation of any sort of feature freeze for the next release
of GRASS GIS I have added a new (long missing) module to GRASS 6.1:
r.in.xyz
This is a replacement for s.in.ascii+ s.cellstats+ s.to.rast in GRASS 5.
The r.in.xyz module will load and bin ungridded x,y,z ASCII data into a
new raster map. The user may choose from a variety of statistical
methods in creating the new raster.
r.in.xyz is designed for processing massive point cloud datasets, for
example raw LIDAR or sidescan sonar swath data.
The r.to.vect module has been given a "-b" flag to skip building vector
topology (same as v.in.ascii) and the v.surf.rst interpolation module
has been modified to not require topology from input points. This gets
around the approx 3 million point memory limit of the vector engine due
to topology overhead. [i.e. you can fill holes with regularized splines]
question for stats experts: Should it be using population or sample
variance? n is generally low so it makes a difference here. In r.univar
it is usually so large not to matter (there we use population variance
for vague reasons).
Thanks to Helena Mitasova for testing and helping me work out the last
few bugs. I would be interested to hear how things go with Large input
files (LFS, bigger than 2gb).
I think this is a wonderful and much needed addition. Thank you very much!
Regards,
Tom
Hamish wrote:
Hi,
In blatant violation of any sort of feature freeze for the next release
of GRASS GIS I have added a new (long missing) module to GRASS 6.1:
r.in.xyz
This is a replacement for s.in.ascii+ s.cellstats+ s.to.rast in GRASS 5.
The r.in.xyz module will load and bin ungridded x,y,z ASCII data into a
new raster map. The user may choose from a variety of statistical
methods in creating the new raster.
r.in.xyz is designed for processing massive point cloud datasets, for
example raw LIDAR or sidescan sonar swath data.
The r.to.vect module has been given a "-b" flag to skip building vector
topology (same as v.in.ascii) and the v.surf.rst interpolation module
has been modified to not require topology from input points. This gets
around the approx 3 million point memory limit of the vector engine due
to topology overhead. [i.e. you can fill holes with regularized splines]
question for stats experts: Should it be using population or sample
variance? n is generally low so it makes a difference here. In r.univar
it is usually so large not to matter (there we use population variance
for vague reasons).
Thanks to Helena Mitasova for testing and helping me work out the last
few bugs. I would be interested to hear how things go with Large input
files (LFS, bigger than 2gb).