[pdal] Extract terrain from TLS point cloud
Luigi Pirelli
luipir at gmail.com
Sun May 26 01:38:29 PDT 2019
writer LAS does not write automatically any field that is not specified by
LAS format => extra_dims option of writers.las have to be set... eg.
"extra_dims":"all"
cheers
Luigi Pirelli
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*
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On Sat, 25 May 2019 at 22:40, Silvia Franceschi <silvia.franceschi at gmail.com>
wrote:
> Sorry Luigi...
> I realized that the filter to use is the filter.ferry with a pipeline like
> this:
> {
> "type":"filters.ferry",
> "dimensions":"NormalZ[0:6]=>Classification[2]"
> },
> or at least, I guess because now I have the problem that the NormalZ field
> do not exist. So my question is: should I write the file after
> filter.normal to have this field or is it possible to use it in the same
> pipeline? from the documentation it seems that it should be possible but I
> can not find an example or something which could help...
> If I have to write the file, should I specify also to insert the NormalZ
> field or is it automatic?
>
> Thank you in advance for the help!
>
> Best regards
>
> Silvia
>
>
>
>
> On Sat, May 25, 2019 at 10:16 PM Silvia Franceschi <
> silvia.franceschi at gmail.com> wrote:
>
>> Dear Luigi,
>> finally I could start to work again on the DTM extraction and I was
>> trying to apply the same commands as in your slides.
>> Unfortunately I am not so smart to understand how to do this kind of
>> assignment in a PDAL pipeline:
>>
>> - normalZ [0:0.6] : Classification ->1
>> - normalZ ! [0:0.6] : Classification ->2
>> - merge soil / no soil
>>
>> I tried using the filter.assign but it seems that I can not assign values
>> of a field while filtering on an other field.
>> And also I can not understand what you mean with merge soil/no soil...
>>
>> The pipeline I did is like this:
>>
>> {
>> "pipeline": [
>> {
>> "type": "readers.las",
>> "filename":"aaa.las"
>> },
>> {
>> "type":"filters.range",
>> "limits":"Classification[2:2]"
>> },
>> {
>> "type":"filters.normal",
>> "knn": 30
>> },
>> {
>> "type":"filters.assign",
>> "assignment" : "normalZ[0:6]=Classification[2]",
>> "assignment" : "normalZ[0:6]!=Classification[1]"
>> },
>> {
>> "type": "writers.las",
>> "filename":"bbb.las"
>> }
>> ]
>> }
>>
>> Could you please address me to the right commands to use to finalize the
>> pipeline?
>>
>> Thank you for the big help!
>>
>> Best regards
>>
>> Silvia
>>
>>
>>
>>
>> On Mon, May 13, 2019 at 9:24 AM Luigi Pirelli <luipir at gmail.com> wrote:
>>
>>> we had similar problems with TLS in urban context regarding first part
>>> pf the cars
>>> you can see our strategy to clean the first PMF effect in slides from
>>> http://slides.com/darango/deck-10-11-12-11-12#/11
>>> to
>>> http://slides.com/darango/deck-10-11-12-11-12#/1
>>> <http://slides.com/darango/deck-10-11-12-11-12#/11>5
>>>
>>> the main idea is to do noise cleaning, PMF, filter on normal, filter on
>>> kdistance, PMF again
>>>
>>> not clear to me if you can have a good discrimination of the ground part
>>> of the threes using normals due to nature of these surfaces.
>>>
>>> cheers
>>>
>>> Luigi Pirelli
>>>
>>>
>>> **************************************************************************************************
>>> * LinkedIn: https://www.linkedin.com/in/luigipirelli
>>> * Stackexchange: http://gis.stackexchange.com/users/19667/luigi-pirelli
>>> * GitHub: https://github.com/luipir
>>> * Mastering QGIS 2nd Edition:
>>> *
>>> https://www.packtpub.com/big-data-and-business-intelligence/mastering-qgis-second-edition
>>> * Hire me: http://goo.gl/BYRQKg
>>>
>>> **************************************************************************************************
>>>
>>>
>>> On Sat, 11 May 2019 at 10:01, Silvia Franceschi <
>>> silvia.franceschi at gmail.com> wrote:
>>>
>>>> Dear all,
>>>> I am trying to extract the ground points from a point cloud of a
>>>> terrestrial laser scanner (TLS) in a forestry environment, but the result
>>>> is not so good as the one I got using data from ALS.
>>>> What I did is:
>>>> 1. denoise the dataset using the filter.outlier and a filter.range for
>>>> Z ranges:
>>>> "type": "filters.outlier",
>>>> "method": "statistical",
>>>> "multiplier": 3,
>>>> "mean_k": 8
>>>> 2. apply a filter.elm and a filter.smrf to the cleaned dataset
>>>> For the SMRF filter I used these parameters:
>>>> "type": "filters.smrf",
>>>> "slope":0.2,
>>>> "window":16,
>>>> "threshold":0.45,
>>>> "scalar":1.2
>>>>
>>>> Unfortunately the resulting ground points contain also the first part
>>>> of of the trunks of the trees, how is it possible?
>>>>
>>>> I am doing some testing changing the values of the SMRF parameters but
>>>> the results are almost the same.
>>>>
>>>> Do some of you have experience with such kind of data? Am I missing
>>>> some operation for filtering? Could you please help me understanding at
>>>> least where to focus the attention to try to obtain something...
>>>>
>>>> Thank you in advance.
>>>>
>>>> Silvia
>>>>
>>>>
>>>> --
>>>> ing. Silvia Franceschi
>>>> Via Roma, 64
>>>> 38030 Castello di Fiemme (TN)
>>>>
>>>> tel: 0039 -3384501332
>>>> _______________________________________________
>>>> pdal mailing list
>>>> pdal at lists.osgeo.org
>>>> https://lists.osgeo.org/mailman/listinfo/pdal
>>>
>>>
>>
>> --
>> ing. Silvia Franceschi
>> Via Roma, 64
>> 38030 Castello di Fiemme (TN)
>>
>> tel: 0039 -3384501332
>>
>
>
> --
> ing. Silvia Franceschi
> Via Roma, 64
> 38030 Castello di Fiemme (TN)
>
> tel: 0039 -3384501332
> _______________________________________________
> pdal mailing list
> pdal at lists.osgeo.org
> https://lists.osgeo.org/mailman/listinfo/pdal
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