The document summarizes using FME for several mapping and geospatial data projects. It describes using FME to generate web map tiles from meteorological forecast models for Pelmorex in near real-time. It also discusses using FME Cloud to dynamically provision compute capacity for tile generation every 12 hours to process large amounts of data. Finally, it mentions that the approach costs $80 per run compared to $300,000 if done on-premises.

1. Around the World with FME

2. Multilevel
Tile Cache
Generation
Engine
LA RIOJA, SPAIN
Ana García de Vicuña
Pablo Martínez
Gobierno de La Rioja

3. MBTiles format
Store pre-cache tiles in a sqlite database to speed web mapping
Implemented in the workbench

4. Mapnik
- Toolkit for making high quality raster maps
- Complex styles and simbology
- FME transformer since FME 2014 (MapnikRasterizer)

5. Multilevel tile cache generation engine
- Raster data
- Vector data
- Text labels
- Parameters (zoom, bbox,…)
MapnikRasterizer =
f(zoom)
Input Apply symbology
WebMaptiler
(GoogleMaps compatible)
Tiled data

6. Multilevel tile cache generation engine
Save unique tiles
Optimizing MBTiles MBTiles database
Mbtiles
f(zoom)
Output
- metadata
- images
- maps
tiles
Water
60%
Land
38%
Mix
2%
Tiles TMS
Tables View

7. http://bit.ly/iderioja
Multilevel tile cache generation engine

8. Ana García de Vicuña
Pablo Martínez
Thank you very much!!!
See you in Barcelona…
View the full presentation at
http://goo.gl/0dqjR4
(en español)

9. 3D DGN to
Multipatch
for City
Engine
UNITED KINGDOM
Konrad Poplawski

10. Thames Tideway Tunnel
Needed:
 a reliable, repeatable
method to transfer 3D
model data between
Bentley MicroStation
and Esri City Engine.
Challenges:
 no direct format
support
 georeferencing
 attribution

11. Basics
 Collada chosen as an interim
format
 DGN is georeferenced
 Python script reads file pairs
based on name
 Batch deploy processes full
sets of models

12. Maintaining Georeferencing
 BoundingBoxAccumulator
 CenterPointReplacer

13. Attribute Handling
 Create attribute lists from original CAD file
 Generate keys for downstream re-attribution
 Clean up attributes

14. Re-attribution
 asdf

15. The Result: 3D Web View
 Esri Multipatch
Feature Classes,
positioned and with
attributes
 Detailed planned
construction available
to all stakeholders in
a browser

16. Data
Migration at
Irish Water
IRELAND
Patrick Daly
Irish Water

17. The Project
 Irish Water formed in
2013 to bring together
water and wastewater
services of 34 Local
Authorities
 Networked data including
nearly 80,000 km of pipe
and hundreds of
thousands of point
features

18. Challenges
 Source: Local
authority data in CIS
and MapInfo datasets
 Not in consistent
formats
 30+ variants in format
and schema
Water Below Ground
Selected Feature Class Feature Count Length /km
Air Valve 34,479
Fittings 493,798
Flow Control Valves 2,932
Hydrants 148,451
Network Meters 28,221
System Valves 229,623
Laterals (Service/Comms) 307,003 6,293
Water Mains (of which 6,055Km
Private) 713,390 61,183

19. Solving with FME
 Destination: complex
ArcGIS Water Utility
Data Model
 30+ sets of
workspaces for
normalization and
migration
 15 hours processing
time

20. Destination: IW Enterprise GIS

21. Irish Water
We completed this project in 9 weeks using FME, migrating
almost 80,000 km of Water & Waste Water Network data. This
scale of project with this timeline would not be achievable
without FME.
Patrick Daly, Asset Register & Data Aggregation Specialist
Irish Water
Irish Water GIS Migration Team
Kelly Brady – GIS Analyst North West
Sean Minogue – GIS Analyst East/Midlands
Sandra Nestor - GIS Analyst East/Midlands
Rónán O’Shea – GIS Analyst South
Mark Healy – Reporting & Information Analyst

22. BIM to GIS
at Mount
Vernon
VIRGINIA, USA
Patrick Gahagan, Esri
Quinn Evans Architects
Mount Vernon Ladies’ Association

23. Mount Vernon
 George Washington’s home, constructed in the late 1800s
 Mount Vernon Ladies’ Association tasked with restoration, interpretation,
and preservation of grounds and structures
 Mansion laser scanned to create architectural-quality HBIM in Revit by
Quinn Evans Architects

24. BIM to GIS via Data Interop
 Revit: Add coordinates and
rotation from project north to
true north
 Export to Revit Archive(.rvz)
with FME Revit Exporter,
attributes to spreadsheet
 Import to ArcGIS with Data
Interoperability Extension
(FME)
 Reconnect attribution

25. A Blended World

26. Viewshed Analysis
 asdf

27. Stakeholder Access to Information
 Browser delivery provides
data to everyone, even
fire suppressant system
designer
 Historical data identifies
plaster from 1950 vs.
1787
 Framing details assist with
optimal routing

28. A navigable, queriable world provides minute detail and the big picture to all
stakeholders – BIM/GIS pros or not.

29. OS 3D Data
Generation:
Using FME to Investigate
the Creation of Point
Clouds from Imagery
UNITED KINGDOM
Nikki Goodwyn
Photogrammetric Surveyor
Ordnance Survey

30. THE CHALLENGE
 SYNCHRONISING TWO OUTPUTS: DIGITAL SURFACE MODEL
(DSM) AND DIGITAL TERRAIN MODEL (DTM)
DSM DTM

31. METHODS
Point Cloud- No Colour ValuesRGB colouring brings points back to reality

32. SOLUTION
FME WORKBENCH

33. INPUT 1:
ORTHO
GEOTIFF
FME
EXTRACTS
Raster
Properties
Including
RGB
Properties
Combined into
RGB & Real64
FME
CONVERTS
DEM (XYZ)
INT32 TO
REAL64
INPUT 2:
DSM
GEOTIFF
Scaled and
Output as
LAZ Point
Cloud
SOLUTION
Input =1km Ortho Tile as
Geotiff
Input= 1km DSM of same
Tile as Geotiff
RasterBandInterpretation
Coercer
RasterPropertiesExtractor
RasterExpressionEvaluator

34. RESULTS
COLOURISED IMAGE-BASED POINT CLOUD OUTPUT

35. Nikki Goodwyn
NGoodwyn@os.uk
02380 055972
THANK YOU

36. UAV-Based
LiDAR Data
Collection &
Analysis
FINLAND
Ville Koivuranta
Sharper Shape Ltd.

37. Next Eagle® by Sharper Shape
 Our service fully automatically detects each vegetation issue that
threatens the transmission network. We prioritize and visualize
the vegetation observations to create ready management plans
and work orders that customer could send to his subcontractors.
 We identified 3-5 times more issues in the areas that our solution
inspected than with traditional methods.
 This enabled need based vegetation management.

38. UAV Solution
 Sharper Shape UAV’s are capable Beyond Visual Line Of Sight
flights with up to 1 hour flight time and up to 8 kg payload.
 Sharper Shapes UAV’s are equipped with high performance LiDAR,
High resolution cameras, onboard computer and storage unit and
high precision IMU (Inertial Measurement Unit)

39. FME in Route Planning
 Economically optimized flight routes are created using FME.
 The elevation information is sourced from existing point
cloud or from national DTM.
 3D shape polylines are uploaded to autopilot and the UAV
follows the pre programmed flight line.

40. Customer Data Integration
 FME is perfect to fulfill customer specific small needs.
 For example one customer wanted to be able to create cross-section
images from network and it was done using FME
 Also all data conversions from customer NIS system to our systems are
done with FME.
 FME is used to create PDF-map of the vegetation issues that can be used
field personnel to do the vegetation clearances.

41. Prioritization Problem
 Distribution companies can have hundreds of thousands of
vegetation issues.
 Vegetation management need to be targeted to where it is most
needed.
 Economically optimized vegetation clearance plans created with
FME can reduce expected losses caused by power interruptions up
to 3-5 times more than legacy methods.

42. Vegetation Management Prioritization
Handle the areas in the
priority order. Proceed with
planning.

43. Conclusions
 Combination of these new
technologies allows us to take
full control of our asset
management.
 Our goal is to create full turn
key solution that is both cheaper
and more effective than any
legacy method.

44. Building a
Spatial Decision
Support System
for Natural Gas
Pipeline Risk
OKLAHOMA, USA
Matt Landry
Frank Yeboah
Williams Companies

48. Gas
Gathering System
R_GGS_FACTS.fmw
[R_GGS_FACTS]
SEGMENT_ID
R_SEGID_FACTS.fmw
[R_SEGID_FACTS]
GISID_ID
R_GISID_FACTS.fmw
[R_GISID_FACTS]
SUBSEG_ID
R_SUBSEGID_FACTS.fmw
[R_SUBSEGID_FACTS]

49. R_SUBSEGID_FACTS.fmw

51. Image Source:
http://www.utc.wa.gov/publicSafety/
Documents/Williams%20Presentation
%20-%20ILI%20Technologies.pdf

53. Leveraging
FME Cloud:
Near Real Time
Global Web Map
Tile Generation for
Meteorology
CANADA
Pelmorex

54. The Project
Produce web map tiles from a worldwide forecast
meteorological model called ECMWF. Layers include:
 Precipitation
 Sea Surface Temperatures
 Ground Temperatures
 Wind speed and direction

55. Challenges
 880,000 tiles need regenerating every 12 hours.
 The maps are time sensitive so the data needs
processing as quickly as possible.
 Each run needs around 80 hours of compute time.
 Their on-premises 10 engine FME Server did not
have powerful enough hardware.

56. Solution
 Leverage FME Cloud elastic processing workflows.
 Provision FME capacity dynamically every 12 hours.
 Leverage AWS services such as S3, SQS and AWS
Lambda.

57. Solution – Dynamically Provision Capacity

58. Solution – Processing Data using SQS

59. Cost Analysis
 Costs $80 per run or $58,000 annually.
 On FME Server to replicate performance they
would need 40 engines (~ $300,000) and a lot of
hardware.

60. Oracle
Spatial to
SAP HANA at
Alliander
NETHERLANDS
Stefan Koster
BI&A Architect

61. Oracle Spatial to SAP HANA at
Alliander

62. The Challenge
 Alliander wants repeatable workflows to move
data from Oracle Spatial/Esri Geo Data
warehouse to SAP HANA for advanced SAP/GEO
BI analysis
 Departments aligning, GIS has FME already
 FME preferred, complex spatial handling
 Will FME do the job?

63. JDBC Technology Preview
 Java Database
Connectivity tech
preview in FME
2015
 Configured for
SAP HANA
 Transformers
massage
geometry into
expected format

64. The Results

65. How did FME do?
 Complete ETL process 2.5 to 3 times faster
than the tested alternative
 Advanced spatial handling of FME enables
automation
 ETL tasks across departments on a common
platform

66. Geostor:
State GIS
Clearinghouse
Cloud Migration
ARKANSAS, USA
Seth LeMaster
Tony Davis
Arkansas GIS OfficePhoto by Chriseast18/ CC BY

67. Overview
Arkansas GIS Clearinghouse
 GIS open data portal for the state of Arkansas
 1222 monthly downloads (2,358 items downloaded)
 Users
 108 registered
 2,387 non-registered

68. Overview

69. The Project - GeoStor
Improve the usability and migrate to the cloud.
 300 vector datasets migrated to PostGIS and AWS
S3
 3TB of raster data
 4TB of historical raster data to AWS Glacier

70. Architecture

71. The Workspace

72. Benefits of Cloud
 Stability – Fault tolerant data storage and Safe
monitor and support FME Cloud architecture.
 Security – Leverage AWS compliance and FME Cloud
security policies.
 Simplicity – Focus on problem not the administration
 Price – 3 times cheaper than on-premises

73. Option 1 – On Site Upgrade
Monthly Yearly Up-Front
DIS Server Space $3,200.00
FME Dekstop $6,000.00
FME Server $12,000.00
Dell Hardware $100,000.00
SQL Server
Windows Server Std (Software Assurance)[5] $595.00
Windows Sevrer Enterprise (Software Assurance) [1] $385.00
Microsoft SQL Server (Software Assurance) $270.00
Microsoft SQL Server Standard Edition (Licenses) $294.00
Microsoft Server Std Edition (License) [6] $1,662.00
Microsoft Windows Server Enterprise Ed (Licences)
[5] $4,490.00
Symantec $680.00
Tape Backups (No server) $2,000.00
Total Recurring Montly Payment $3,200.00
Total Recurring Yearly Payment $26,376.00
Total One Time Cost (Every 3 yrs) $102,000.00
Total Three Year Cost $296,328.00
True Monthly Cost (/36 months) $8,231.33
Intangiable Costs
Hardware Maintenance Time
DIS Process
Non-Scaleable

74. Option 2 – Cloud
Monthly Yearly Up-Front
FME Dekstop $6,000.00
FME Cloud $14,400.00
EC2 Instance (m3.large) $395.81
AWS Storage (EBS 780GB) $77.11
AWS Storage (S3 2TB) $61.30
AWS Storage (Glacier 4.2 TB) $42.41
Total Recurring Montly Payment $576.63
Total Recurring Yearly Payment $20,400.00
Total Three Year Cost $81,958.68
True Monthly Cost (/36 months) $2,276.63
Our rack space costs (real estate on our data center floor) $3,800 per month. Add to that the hardware costs,
etc and you start to see why moving to the cloud was a no brainer for us. Anthony Davis, State Arkansas

75. Thank You!
 Questions?
 blog.safe.com