<div dir="ltr"><div class="gmail_default" style="font-size:small">Dear Luigi,</div><div class="gmail_default" style="font-size:small">finally I could start to work again on the DTM extraction and I was trying to apply the same commands as in your slides.</div><div class="gmail_default" style="font-size:small">Unfortunately I am not so smart to understand how to do this kind of assignment in a PDAL pipeline:</div><div class="gmail_default" style="font-size:small"><br></div><div class="gmail_default" style="font-size:small">- normalZ [0:0.6] : Classification ->1<br>- normalZ ! [0:0.6] : Classification ->2<br>- merge soil / no soil<br></div><div class="gmail_default" style="font-size:small"><br></div><div class="gmail_default" style="font-size:small">I tried using the filter.assign but it seems that I can not assign values of a field while filtering on an other field.</div><div class="gmail_default" style="font-size:small">And also I can not understand what you mean with merge soil/no soil... </div><div class="gmail_default" style="font-size:small"><br></div><div class="gmail_default" style="font-size:small">The pipeline I did is like this:</div><div class="gmail_default" style="font-size:small"><br></div><div class="gmail_default" style="font-size:small">{<br> "pipeline": [<br> {<br> "type": "readers.las",<br> "filename":"aaa.las"<br> },<br> {<br> "type":"filters.range",<br> "limits":"Classification[2:2]"<br> },<br> {<br> "type":"filters.normal",<br> "knn": 30<br> },<br> {<br> "type":"filters.assign",<br> "assignment" : "normalZ[0:6]=Classification[2]",<br> "assignment" : "normalZ[0:6]!=Classification[1]"<br> },<br> {<br> "type": "writers.las",<br> "filename":"bbb.las"<br> }<br> ]<br>}<br></div><div class="gmail_default" style="font-size:small"><br></div><div class="gmail_default" style="font-size:small">Could you please address me to the right commands to use to finalize the pipeline?</div><div class="gmail_default" style="font-size:small"><br></div><div class="gmail_default" style="font-size:small">Thank you for the big help!</div><div class="gmail_default" style="font-size:small"><br></div><div class="gmail_default" style="font-size:small">Best regards</div><div class="gmail_default" style="font-size:small"><br></div><div class="gmail_default" style="font-size:small">Silvia</div><div class="gmail_default" style="font-size:small"><br></div><div class="gmail_default" style="font-size:small"><br></div><div class="gmail_default" style="font-size:small"><br></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Mon, May 13, 2019 at 9:24 AM Luigi Pirelli <<a href="mailto:luipir@gmail.com">luipir@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr">we had similar problems with TLS in urban context regarding first part pf the cars<div>you can see our strategy to clean the first PMF effect in slides from</div><div><a href="http://slides.com/darango/deck-10-11-12-11-12#/11" target="_blank">http://slides.com/darango/deck-10-11-12-11-12#/11</a></div><div>to</div><div><a href="http://slides.com/darango/deck-10-11-12-11-12#/11" target="_blank">http://slides.com/darango/deck-10-11-12-11-12#/1</a>5</div><div><br></div><div>the main idea is to do noise cleaning, PMF, filter on normal, filter on kdistance, PMF again</div><div><br></div><div>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.</div><div><br></div><div>cheers<br clear="all"><div><div dir="ltr" class="gmail-m_4016750239997626244gmail_signature"><div dir="ltr"><div><br></div><div>Luigi Pirelli<br><br>**************************************************************************************************<br>* LinkedIn: <a href="https://www.linkedin.com/in/luigipirelli" target="_blank">https://www.linkedin.com/in/luigipirelli</a><br>* Stackexchange: <a href="http://gis.stackexchange.com/users/19667/luigi-pirelli" target="_blank">http://gis.stackexchange.com/users/19667/luigi-pirelli</a><br>* GitHub: <a href="https://github.com/luipir" target="_blank">https://github.com/luipir</a><br>* Mastering QGIS 2nd Edition:<br>* <a href="https://www.packtpub.com/big-data-and-business-intelligence/mastering-qgis-second-edition" target="_blank">https://www.packtpub.com/big-data-and-business-intelligence/mastering-qgis-second-edition</a><br>* Hire me: <a href="http://goo.gl/BYRQKg" target="_blank">http://goo.gl/BYRQKg</a><br>**************************************************************************************************</div></div></div></div><br></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Sat, 11 May 2019 at 10:01, Silvia Franceschi <<a href="mailto:silvia.franceschi@gmail.com" target="_blank">silvia.franceschi@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div class="gmail_default" style="font-size:small">Dear all,<br></div><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div style="font-size:small">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.</div><div style="font-size:small">What I did is:</div><div style="font-size:small">1. denoise the dataset using the filter.outlier and a filter.range for Z ranges:</div><div style="font-size:small"><div> "type": "filters.outlier",</div><div> "method": "statistical",</div><div> "multiplier": 3,</div><div> "mean_k": 8</div></div><div style="font-size:small">2. apply a filter.elm and a filter.smrf to the cleaned dataset</div><div><div>For the SMRF filter I used these parameters:</div><div> "type": "filters.smrf",</div><div> "slope":0.2,</div><div> "window":16,</div><div> "threshold":0.45,</div><div> "scalar":1.2</div></div><div style="font-size:small"><br></div><div style="font-size:small">Unfortunately the resulting ground points contain also the first part of of the trunks of the trees, how is it possible? </div><div style="font-size:small"><br></div><div style="font-size:small">I am doing some testing changing the values of the SMRF parameters but the results are almost the same. <br></div><div style="font-size:small"><br></div><div style="font-size:small">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...</div><div style="font-size:small"><br></div><div style="font-size:small">Thank you in advance.</div><div style="font-size:small"><br></div><div style="font-size:small">Silvia</div><div style="font-size:small"><br></div><div style="font-size:small"><br></div></div></div></div></div></div></div>-- <br><div dir="ltr" class="gmail-m_4016750239997626244gmail-m_-7584620376355790015gmail_signature"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div style="font-size:small">ing. Silvia Franceschi<br>Via Roma, 64</div><div style="font-size:small">38030 Castello di Fiemme (TN)<br><br>tel: 0039 -3384501332</div></div></div></div></div></div></div></div></div></div>
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</blockquote></div><br clear="all"><div><br></div>-- <br><div dir="ltr" class="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div style="font-size:small">ing. Silvia Franceschi<br>Via Roma, 64</div><div style="font-size:small">38030 Castello di Fiemme (TN)<br><br>tel: 0039 -3384501332</div></div></div></div></div></div></div></div></div>