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Using an LAS dataset to create a DEM or TIN elevation model is useful since the model will be based on a large variety of point coordinates.Using the building footprints as a clipper, we can remove the unwanted returned building points for the surface model.This is a quick and easy way to create an accurate ground surface model, especially if the point cloud data you are working with has inaccurate classification values or does not have a classification component at all.
If the point cloud data that you are working with does have a classification component, consider using the custom transformer “PointCloudLASClassifier” instead of a building shapefile as seen in examples in thisarticle.
lastodem-lastreturn-nobuildings.fmwt
las-surfacemodelling-files.zip
This workflow removes the points that are within the building footprints so that they are not used in the surface modeling.Often the building footprints of the shapefile do not align perfectly with the point cloud buildings.To overcome this issue, the shapefiles are buffered before using them as clippers.This can be seen in this workspace:
The images below show the clipper output and final DEM.The output from this workspace has a lot of noise and is not very smooth.This is likely from vegetation and other features surroundings the buildings.
Below are two additional workspaces that demonstrate more advanced ways of viewing surface models with buildings that produce smoother outputs.
lastodemwithtiling-lastreturn-nobuildings.fmwt
las-surfacemodelling-files.zip
The nature of different data sources can lead to noticeable differences in performance.If the clippers (building footprints) are numerous and they are spread all over the point cloud like we have, then the translation time may be worse than in cases with few clippers or when clippers are localized in a certain area.
Tiling a point cloud before a clipping operation can significantly improve performance.Adding aTilerto the workspace above will process the data faster.The Tiler should be added after the LAS reader and before the PointCloudSplitter.Parameters in our workspace include “Tiling Type” set to “Number of Tiles”, “Number of Horizontal Tiles” set to 2 and “Number of Vertical Tiles” set to 3.The workspace below shows the added Tiler between the LAS reader and PointCloudSplitter.
The images below show the clipper output and final DEM, both after using the Tiler transformer.The output from this workspace is smoother than the previous output, especially near the bottom where there are many buildings.
三角irregular networks (TINs) are complex and vector based models.However, they are more realistic representations of a surface because each node of the triangle represents an exact elevation point rather than an averaged one like with DEMs.Creating a complex model like a city from LAS data only requires one tool called the TINGenerator transformer in FME.We can get some interesting 3D results with realistic colours by generating a TIN from a point cloud with an ortho texture on it.This is a very simple process:
This process was a quick way to make an interesting 3D city model:
The quality of the model depends on surface tolerance parameter in TINGenerator.
hi there
I tried your "LAS to PDF 3D (DEM and TIN).fmwt"
and it gives me at the end of the run 0 features have been translated.
Any thoughts why am I getting this?
regards
Hi@heikal我刚试过,没问题。是我的ou trying to use it with your own data?Be aware there is a Clipper in the workspace, which is clipping the point cloud with an area feature, so this example will work with the data provided in the template.The workspace have feature counts on the connections after you run your workspace.Find where you have 0 features leaving a transformer and that will give you a clue as where to make adjustments.We also have an nice article on debugging workspaceshttps://knowledge.safe.com/articles/19301/root-cause-analysis-guide.html
You are welcome to contact our support team with your data/workspace if you need further assistance.https://support.safe.com/knowledgeSubmitCase
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