Hi List,
We are using v.net.distance to calculate the shortest path between two
points on a river network, but have been experiencing some issues in
both grass 7.4 and 7.9. In some instances, we need to add a new vector
line (stream) to the river network, which we have done using v.patch as
Hi Moritz,
Thanks for the hint. Indeed, I used the default region_growing method, not
mean_shift.
Maybe a warning message if band_suffix is given but not mean_shift method would
avoid confusion?
Or a note in the manual (or both)...
Cheers
Stefan
Fra: Moritz
Thanks for the answer.
*v.class.mlR segments_map=gbbb@PERMANENT training_map=tainning@PERMANENT
train_class_column=fd output_class_column=vote output_prob_column=prob
folds=5 partitions=10 tunelength=10 weighting_metric=accuracy*
*and the same error with this command: *
*v.class.mlR
El mié., 20 nov. 2019 7:11 a. m., Laura Poggio
escribió:
> Dear Markus,
> I will do it as soon as possible.
> Thanks
> Laura
>
> On Tue, 19 Nov 2019 at 10:11, Markus Neteler wrote:
>
>> Hi Laura,
>>
>> Today the EPEL package reached "testing":
>>
>>
Dear Markus,
I will do it as soon as possible.
Thanks
Laura
On Tue, 19 Nov 2019 at 10:11, Markus Neteler wrote:
> Hi Laura,
>
> Today the EPEL package reached "testing":
>
> https://bodhi.fedoraproject.org/updates/FEDORA-EPEL-2019-8d020579ef
>
> You may want to test it and leave "karma" there
On 20/11/19 11:55, Giuseppe Cillis wrote:
Hi,
I'm trying to use this module for classification of an old aerial photos.
After a segmentation (i.segment and i.segment.stats), I would to use a
machine learning approach for the real classification.
I tried with v.class.mIR which use also R.
But
On 20/11/19 11:19, Stefan Blumentrath wrote:
Hi,
I thought that if I give the band_suffix option in i.segment it
produces bands with modified band values for all input bands. However,
the option does not seem to have effect (no modified output bands are
produced). Did I do or understand
Hi,
I'm trying to use this module for classification of an old aerial photos.
After a segmentation (i.segment and i.segment.stats), I would to use a
machine learning approach for the real classification.
I tried with v.class.mIR which use also R.
But there is an error and I don't know how to solve
Hi,
I thought that if I give the band_suffix option in i.segment it produces bands
with modified band values for all input bands. However, the option does not
seem to have effect (no modified output bands are produced). Did I do or
understand something wrong?
Cheers
Stefan