LiDAR (“Light Detection and Ranging”) is a method of remote sensing that uses light to measure ranges. LiDAR systems generate many component measurements that result in valuable spatial data. All of this information results in massive files that are bursting with potential, but limited in use by their size and complexity. In this webinar, learn how data integration techniques can help you get the most out of LiDAR and point cloud data. We’ll cover how to: - Quickly process point clouds and integrate them with other data sources - Use LiDAR for 3D city modelling - Make a digital terrain and surface model from a point cloud - Integrate programs like LAStools into your workflows By applying data integration automation, you save time, reduce manual effort, and ensure you get the most out of your LiDAR data.

1. 5 Ways to Optimize
Your LiDAR Data

2. Meet the Presenters
Kailin Opaleychuk
Technical Support Specialist I,
FME Desktop
Richard Mosley
Technical Support Specialist II,
FME Server
Jovita Chan
Technical Support Specialist II,
FME Desktop

3. Agenda
● LiDAR Overview
● 5 Ways to Improve Your Lidar Workflows
1. Simplify the process of transforming and integrating point clouds
2. Prepare your data
3. Create (and automate the creation of) surface models
4. Visualize solutions by using LiDAR for 3D City Modelling
5. Expand your toolset with FME Hub and Third Party Tools
● Q&A

4. LiDAR Overview

5. What is LiDAR?
● LiDAR stands for Light
Detection and Ranging
● LiDAR is used to collect and
create point clouds
● Point clouds are sets of
points that describe an
surface

6. Supported Point Cloud Formats
● E57
● Cesium 3D Point Cloud
● LAS/LAZ
● Mojang Minecraft
● Point Cloud data (PCD)
● Point Cloud XYZ (XYZ)
● Oracle Spatial Point Cloud
.... and more!

7. Point clouds can help you produce
amazing 3D visualizations!

8. The problem with
point clouds...
They can be huge.
Theyʼre kinda complicated if you are
new to working with them.
There can be limitations.
Itʼs hard to integrate them with other
data.
Working with them can be expensive.

9. FME®
Integration Platform
Connect. Transform. Automate.
FME Desktop FME Server FME Cloud
Build & Run Data Workflows Automate Data Workflows
(on-premises)
Automate Data Workflows
(cloud)
Get a free trial of FME Desktop and FME Server at safe.com

10. Pointing you in the
right direction with FME
● Create a surface model
● Combine with other formats
● Split datasets
● Volume/point reduction
● Clip to a specific region or AOI
● Perform calculations (stats)
● Point Cloud splitting

11. 1. Simplify the process of transforming
and integrating point clouds using FME’s
Point Cloud Transformers.

12. Our transformers simplify the process of
transforming and integrating point clouds.
Combining transformers into workspaces
creates powerful, repeatable solutions.

13. PointCloudPropertyExtractor

14. PointCloudCombiner

15. PointCloudThinner

16. 2. Prepare your data

17. Data Preparation
● Clip a Point Cloud
● Noise removal
● Creating an extent or AOI
● Tiling

18. 3. Create and automate surface models

19. How FME Understands Point Clouds
● Point Cloud as 1 feature
● Each point has is own component
values
● Each individual component value
can be altered without it being an
attribute
● Attributes from other sources can
be added as components.

20. Understanding Components Vs. Attributes
Attributes
Components
Each point has its own
component values

21. Clip, Tile, and Thin
Especially when designing workflows use smaller sections to
speed up development

22. Demo

23. DEM
Parameters
Set appearance on: Front
Side
Texture Mapping Type:
Top Down Georeferenced

24. Digital Surface Model
Or any other classification model….

25. Non-classified data

26. Demo

27. Automate and Self Serve
Self-Service
● Geometry Picker
○ https://community.safe.com/s/article/using-the-geometry-picker-in-fme-server
Speed up
● Dynamic Engines
○ 1 engine can process 1 workspace at a time. So if you need some performance gains split your workflow and process smaller amounts of data
on more engines.
○ https://community.safe.com/s/article/getting-started-with-dynamic-engines

28. 4. Visualize solutions by using LiDAR for
3D City Modelling

29. Demo: Creating 3D City Models
● Goal: Visualize the city as an accurate 3D model
to ensure developments are integrated well in their neighborhoods and to inform
future decisions
● Obstacles: Large datasets, different formats, which can be difficult to work with.
● Solutions: Use FME to extract needed information from LiDAR,
integrate with other formats like PNG textures and SketchUp models, and apply
that to a 3D City Model

30. Source Data
● AutoCAD (DWG) - building
outlines
● Point Cloud (LAS)
● MapInfo (MiTAB) - Park
polygons
● GeoTIFF (TIFF) - 4
orthoimages

31. Source Data Continued
● 3D SketchUp (SKP)
models
● PNG/JPEG images - for
wall textures
Wall texture from:
https://pxhere.com/en/photo/1421981
SketchUp Tree from:
https://3dwarehouse.sketchup.com/
model/e18aebbf-2859-47f1-805c-8a4f
946f5893/Skatter-Tree-02

32. Goals for this workspace
1. Create 3D buildings using LAS and DWG building outlines
2. Add PNG and orthophotos (GeoTIFFs) as textures on 3D models
3. Filter data to get point features of trees and replace them with
instances of a 3D tree model

33. Demo

35. Workspace Highlights
1. Create 3D buildings using point cloud and vector building outlines
https://community.safe.com/s/article/point-cloud-to-3d-terrain-model-with-build
ings-dwg
2. Add image and orthophotos as textures on 3D models
3. Filter data to get point features of trees and replace them with instances of a 3D
tree model
https://community.safe.com/s/article/derive-a-boundary-from-a-lidar-point-cloud
https://community.safe.com/s/article/creating-and-using-geometry-instances

36. Tips and Tricks: Methods
● Similarities to raster processing
○ Use feature caching strategically
○ Tiling large datasets
● Knowing how FME works with point clouds
○ FME treats a point cloud as a single entity
■ For many operations, use Point cloud specific transformers (attributes vs.
components)
○ Writing it out to a format that doesnʼt support point clouds means youʼre actually
writing a bounding box
■ Coerce to multipoints, surface, or raster

37. Tips and Tricks: Transformers
3D Geometry
● TINGenerator - creates surface models
● Extruder - extrudes outline features (ex. buildings)
Applying textures
● AppearanceSetter - to add image textures, pay attention to the Texture
Mapping Type parameter

38. 5. Expand your toolset with FME Hub
and Third Party Tools

39. PointCloudSpatialThinner
This transformer spatially thins
point clouds by the user-defined
resolution.
IFMEPointCloud: 4377463 Points
IFMEPointCloud: 4025219 Points

40. Demo

41. PointCloudStatsRasterizer
Replaces a point cloud with
a raster which represents
statistical values of a
component of the source
point cloud.

42. Demo

43. LAStools
LAStools.lasground: for bare-earth identification: it classifies the LiDAR points into ground points (class =
2)
LAStools.lasheight: computes the height of each LAS point above the ground.
LAStools.classify: classifies buildings and high vegetation (i.e. trees).
LAStools is produced by Rapid Lasso GmbH

44. 1. Read in LAS file using the ASPRS LiDAR Data Exchange Format (LAS) Reader

45. 2. Remove existing classification

46. 2. Use QuickAdd to add LAStools.ground

47. 2. Now that ground is classified, add LAStools.lasheight

48. Tips and Tricks: LAStools
● Disable feature caching
● If tools are not running properly and or erroring without much indication, try
purging your temp file (FME Workbench > Tools > Purge Temporary Folder)
○ LAStools.lasheight_Terminator (TestFactory): LAStools.lasheight_Terminator: Termination Message: 'Translation
Terminated. lasheight.exe wasn't executed successfully'
○ A fatal error has occurred. Check the logfile above for details
● If possible, shorten the file path as much as possible in the transformers
○ C:devLAStoolsLAStools-binbinexecutables.exe

49. Summary

50. Working with LiDAR data
shouldn’t crash your computer.
With the right tools and knowledge, you can feel
confident integrating LiDAR with the rest of your
data and get the most out of LiDAR data.

51. Improve your LiDAR
workflows by:
1. Simplifying the process of transforming
and integrating point clouds using
transformers.
2. Preparing your data.
3. Automate and Scale with FME Server
4. Visualizing solutions by using LiDAR for
Surface Models and 3D City Modelling.
5. Expanding your toolset with FME Hub
and Third Party Tools.

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53. Q&A

54. The Peak of Data Integration 2022 UC
August 24-26, 2022 Vancouver, Canada
Register now

55. Check out our upcoming
& on-demand webinars:
safe.com/webinars

56. Thank you!
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