Maximize the possibilities of your LiDAR data with FME. Through demos, you’ll learn how to extract the full value of point clouds by quickly processing and combining them with other data sources. We’ll also show you real-world examples using LiDAR for 3D city modelling & viewshed analysis, with specific takeaways that can be applied to your own data. Plus, find out how to integrate command-line programs like LAStools into your FME workflow.

1. How to
Get the Most Out of LiDAR Data

2. Hello.

3. Get answers fast with LiDAR experts online:
Dale Lutz Dave Campanas

4. Overview
The game plan >
● What can you do with your
point clouds in FME?
○ Demo: DTM and DSM
● Example: LiDAR for 3D city
modelling
● Example: Calculating a view
of the water
○ Demo: Viewshed
analysis
● How to integrate a 3rd-party
library into your FME
workflow
○ Demo: LAStools in
action
● Conclusion and Q&A

5. The problem with
point clouds
● They’re huge.
● They’re kinda complicated.
● It’s hard to integrate them
with other data.

7. Translate between data formats
LAS (plus LAZ, zLAS)
XYZ
ASTM E57
Oracle Spatial
Pointools POD
RIEGL Laser Scan Database
Terrasolid TerraScan
Z+F LaserControl ZFS
Minecraft
CARIS Spatial Archive
Autodesk ReCap
Cesium (in progress)
RasterFormats
VectorFormats

8. Inspect point components and values

10. Transform

11. Transform

12. Transform

13. Transform

14. Transform

15. Transform

16. Transform

17. Transform

18. Transform

19. Demo
Merge component values
from one point cloud to
another.
Sort a point cloud by the
values of components.

20. OutputInput
DTM DSM

22. OutputInput

23. Performance
● 17,000,000,000 points generated from
77,000,000 linear features and compared against a
102,000 x 76,000-pixel raster in just 24 hours
● Parallel Processing: http://fme.ly/parallel

24. STUART JONES, MCIP, RPP | SENIOR PLANNER

25. The Project
● The City of Surrey developed a 3D
model in ArcGIS by combining LiDAR
data and vector layers using FME.
● The model is used by city planners to
visualize new developments and
ensure their compatibility with
existing neighbourhoods.

26. Source data

27. The workspace

28. Output

29. MICHAEL OBERDRIES

30. The Project
● Identify which land parcels in New
Zealand have a view of the water.
● Use this and other factors to determine
property value.
● 1.5 million parcels and 77 million lines of
sight result in over 17 billion DEM
interpolation points

31. Demo

32. Output

33. MARTIN ISENBURG

34. Integrating third-
party tools
in your FME workflow
By building a custom
transformer, you can read,
write, and process data
however you want.
blog.safe.com http://fme.ly/3rdparty

35. FeatureReader and FeatureWriter

36. LAStools in action
rapidlasso.com

37. LASpublish
http://potree.org/ - Potree is a WebGL based viewer for large point
clouds by Markus Schütz
http://fme.ly/surrey

38. Summary
“Something this amazing
should not be this easy.”
-Don
● What can you do with your
point clouds in FME?
○ Demo: DTM and DSM
● Example: LiDAR for 3D city
modelling
● Example: Calculating a view
of the water
○ Demo: Viewshed
analysis
● How to integrate a 3rd-party
library into your FME
workflow
○ Demo: LAStools in
action
● Conclusion and Q&A

39. Q&A
Chat your questions into
the user control panel.

40. Free Candy
(Resources)
● Blog posts: 3rd party
integration, LiDAR
○ blog.safe.com
● Join the FME Community
○ knowledge.safe.com
● Live FME Training (Free!)
○ training.safe.com

41. Thank you!
Live chat us anytime at www.safe.com