Aim: "To seek out innovative FME users throughout the galaxy, sharing their stories and ideas to inspire you to take your data where no data has gone before."

1. Twitter Hashtag: #FMEWT
Ken Bragg @KenAtSafe
European Services Manager
Safe Software
April – June 2013

2. Introducing FME 2013

7. Our Mission:
To seek out innovative FME users
throughout the galaxy, sharing
their stories and ideas to inspire
you to take your data where no
data has gone before.

11. UVM Systems - Austria
 The Mission: Create CityGRID navigable 3D
worlds with thousands of individual 3D models
 The Solution: Automate model and terrain data
preparation and QA tasks with FME

12. UVM Systems CityGRID
 Custom transformers collect linework, orthophotos, and
create models, and flag for manual intervention if questions
encountered (hole in roof, building footprint exceeds roof
area)
 FME also used to prepare
terrain from ortho, point
cloud, terrain models
 All data combined in usernavigable “scene” using
CityGRID tools to view
Proposed Windpark, view from village

13. UVM Systems CityGRID
 New Freight Train Bypass Flythrough

14. San Antonio Water System – USA
Toni Jackson & Larry Phillips
 The Mission: Integrate multiple systems and data
types across departments, while adopting a new
Oracle-based asset management system.
 The Solution: Use Esri’s FME-based Data
Interoperability Extension to handle it, and save
a pile of money at the same time.

15. San Antonio Water System
“The Data Integration gave us the opportunity to correct, cleanse, reconcile
and expose data that had been inaccurate. It’s also a chance for our team to
build new workflows, validation processes and rules to ensure accurate data.”

16. San Antonio Water System
Effective data
affects all of
SAWS

17. San Antonio Water System
New developments –
 QA/QC streamlined – 50
data integrity checks run
and reported on weekly
 Syncing GIS and asset
management data views
across company
"Without FME, we would have
needed to double our team to
accomplish what we did with a
few people's effort. In fact, we
estimate the money saved in
our first year alone is nearly
$1,000,000.” - 2011

18. The GeoInformation Group - UK
Phil Dellar
 The Mission: To produce the most detailed and
comprehensive large scale mapping database,
called UKMap.
 The Solution: Use FME to integrate, combine,
verify and transform data that has been collected
from survey

19. The GeoInformation Group


Data collected manually in the field are
processed automatically using FME
Efficient and repeatable data publication
routines achieved

20. The GeoInformation Group
 Multi-layered geodatabase
 1:1000 topo layer
 Thematic layers
 5k – 100k
 Created from high resolution
aerial imagery and field survey.
 Data compiled and cleaned
using FME workbench ensuring
standards are achieved

21. The GeoInformation Group
 Over 15 million records
 Nine layers
 37 attribute fields
 Typically 10,000 polygons per km2
 Averaging 1,200 addresses
 258 Land use codes
 73 – 300Mb per km2
 Stored in Oracle

22. Kansas DOT Division of Aviation - USA
 The Mission: Preserve airport usability to ensure
that air ambulance service is readily available to
the public.
 The Solution: Build a public online tool to
illustrate and evaluate the effects of proposed
vertical constructions on airport airspace

23. KDOT Aviation
The Kansas Airspace Awareness Tool (Google Earth)
 FME generates 3D airspace polygons using mathematical
interpretations of verbose FAA descriptions
eg. “Below 7,000 ft AGL within an 8 mile radius of X.”
 Users place proposed vertical constructions – windmill, cell
tower, office building – and check for conflicts with airspace and
FAA requirements
 FME handles updates to respective airport and FAA data

24. KDOT Aviation

25. Gobierno de La Rioja – Spain
Ana García de de Vicuña
Ana García Vicuña
Ruiz de Argandoña !
 The Mission: Generate land cover classification
from RapidEye multispectral images for
agricultural analysis – without required
algorithms available in remote sensing software
 The Solution: Use FME to do it, in a single
workspace.

26. Gobierno de La Rioja

Step 1 – Convert each pixel’s Digital Number (DN) to a radiance
value by multiplying the DN by the radiometric scale factor.

Step 2 – Convert radiance values to ToA (top of atmosphere)
reflectance values, taking
into consideration variables
such as:
 distance from the sun and
 angle of incoming solar
radiation.
Defining variables to be used in the workspace

27. Gobierno de La Rioja

Step 1: RapidEye image is read by FME, and the
ExpressionEvaluator defines formulas for each band.
Solar azimuth angle formula in FME
Distance between the sun and earth in FME

28. Gobierno de La Rioja
 Step 2:
RasterExpressionEvaluator
performs ToA calculations in
each band.
 Step 3:
Use another
RasterExpressionEvaluator to
calculate vegetation indexes
(NDVI, TCARI, and OSAVI).
The results are written to TIFF.

29. Gobierno de La Rioja
 asdf
Vegetation index image (NDVI, OSAVI and TCARI values in raster point info)

30. Gobierno de La Rioja

31. CN Railway - Canada/USA
Yves St-Julien
 The Mission: Optimize operations at North
America’s only transcontinental rail network, with
over 20,000 route-miles of track.
 The Solution: Use FME Desktop and FME Server
to deliver automated, real time, or event-driven
solutions to almost every CN group and practice.

32. CN Railway
 LiDAR processing extracts surface and track
features to generate alignments, corridors, and
slope analysis

33. CN Railway
 FME Server brings spatial to real time event
processing

34. CN Railway
But wait, there’s more!
 Grid > polygon cellular coverage analysis
 SQL Server decommissioning to Oracle Spatial
 GPS point enhancement with network and geofence data –
7,000,000 points per hour
 Point cloud indexing
 AutoCAD® Map 3D <> MapGuide interface with FME Server
REST services

35. WhiteStar Corp - USA
 The Mission: Automate a manually intensive land
grid data ordering and fulfillment system for
external customers.
 The Solution: Use FME Server’s email protocol
support to process and fulfill emailed data orders
– in the cloud.

36. WhiteStar Corp

37. WhiteStar Corp

38. WhiteStar Corp
 Decoding email and processing a data order

39. City of Hamilton Public Health Unit - Canada
Shane Thombs
 The Mission: Automate a manual process
combining spreadsheets, databases, GIS, and
statistical analysis.
 The Solution: Use FME to build a reporting tool in
Google Earth, reducing report generation time
from one week to 12 minutes.

40. City of Hamilton
 West Nile Virus tracking uses statistical and spatial
analysis of field observations over time
 Geomedia® Pro, databases, and spreadsheets (for
charting) were part of manual process
 Replaced with FME to combine all functions and
generates KML
 Reporting tool is now interactive, in Google Earth

41. City of Hamilton
Key Transformers
 StatisticsCalculator – looks
for changes/trends that need
attention
 WebCharter –chart display
 StringConcatenator – builds
URLs for Google Charting API

42. City of Hamilton
 Automating repetitive tasks = huge time savings,
reduced reliance on single specialists/points of
failure
 Faster report availability supports quicker
decisions on level of risk and disease control
activities
 Creative transformer use opens up new
possibilities

43. Nuclear Power Plant Modeling
“When you have an FME Hammer, every data
transformation problem is a nail…”

44. Sweco – Sweden
Ulf Månsson and Johan Sigfrid
 The Mission: Create a 3D model to assist in
decommissioning a 1970s-era nuclear plant –
with only digitized 2D CAD As-Builts as a source.
 The Solution: Use FME to georeference, interpret,
and project the 2D data into a 3D model.

45. Sweco
 Georeference As-Builts using control point files
 Separate floors and elevate to true height above
ground
 Define and attribute rooms
 Set wall thickness and extrude to 3D
 Punch out holes for rooms spanning floors
vertically
 Generate one-meter square grid for recording
measurements, inside and outside

46. Sweco
 Combined
with geology,
surface, and
sampling data
 Output to 3D
PDF and 3D
DWG

47. Waterford City Council - Ireland

48. FME Insider Article

49. FME Case Study
Dublin Region Project Office (DRPO)
 Water Web
 http://cdn.safe.com/resources/casestudies/CaseStudy_WaterWeb.pdf

50. Fingal County Council
(Dublin Regional Water GIS)- Ireland
 The Mission: Provide single enterprise
database of water and drainage data for
the region
 The Solution: Use FME to migrate
 FRAMME and GeoMedia Water
 SUS 25 Drainage
Into single Oracle Spatial central database

51. Fingal County Council
 FRAMME 2 Oracle
 7 FRAMME Segments - Each
segment has unique number
 Network split also across CAD
files
 Attribute stored in Oracle
database
 Key is to Maintain
connectivity
 Remove duplicate records
using the matcher

52. Fingal County Council
CIS 2 Oracle

AttributeValueMapper
 CIS uses a lot of numeric pick lists
 Value Mapper was invaluable for assigning
the matching G/Tech attribute values

FeatureMerger
 Assigned Feature relationships.
 Relationships were contained in a number
of different tables
 The Feature Merger moved the
attributes/geometry required to create a
relationship connection from one feature
to another

53. Fingal County Council
SUS 25 to Oracle




There is no SUS 25 reader in FME
So we wrote a utility to write to
CSV
And loaded the CSV direct to
Oracle
Used the SQLExecutor to generate
the next oracle sequence for
G/Tech

54. Fingal County Council
 Must know the model
 Need to know feature numbers & levels
 If don’t know the model need to understand
FRAMME, MDL, SUS 25, GeoMedia (CIS)
 Logging of invalid data is important for future
correction
 3 Run Migration
 3 Full dry runs between FAT and UAT
 Before 3 week data Freeze


55. Are YOU a Trekker?
Share your FME stories with your
compatriots across the galaxy!
Send them to the FME Insider –
fmeinsider@safe.com

56. Coming up next!

57. pragmatica inc. – Japan
Takashi Iijima
 The Mission: Estimate radioactive material
concentrations in agricultural water supply
catchments near Fukushima
 The Solution: Use FME to interpolate tabular
regional observation data for catchment areas

58. pragmatica inc.
 Source data:
 excel of observations, cesium
concentrations, and locations
 Shape irrigation catchment areas
 Observation points are not
coincident with catchments
 Create a surface model using Z
for the cesium value

59. pragmatica inc.
Two methods required:
Delaunay triangulation and linear
interpolation
 Uses observation points as vertices,
divide catchment polygons
 Interpolate values at center of gravity
 Calculate area-weighted average of
catchment area parts
Voronoi decomposition and Tiessen
method
 Use observation points as seeds
 Divide catchment areas by Voronoi
edges
 Calculate area-weighted average

60. pragmatica inc.
Triangulation
Voronoi Domains

61. 52° North – Germany
Simon Jirka, 52° North and Christian Dahmen, con terra
 The Mission: To create a prototype system using
sensors to assist ships in safe passage under
bridges on inland waterways.
 The Solution: Use FME Server to calculate and
monitor available clearance and ship height,
sending notifications if danger exists.

62. 52° North
Data Sources:
 Onboard Ships: Automated Identification System
(AIS) send Ship ID, position, course, speed, height,
and current draft (distance below water)
 On the river: sensor network monitors water level,
up to once per minute
 Static database: contains bridge locations and
clearance from water reference level

63. 52° North
Workflow:
 When captain subscribes to the service, the ship’s AIS sends
data to FME Server, which tracks its position.
 As a ship approaches a bridge, water level (from sensors) is
compared to bridge height, providing available clearance.
 Clearance is compared to current height above water (ship
height minus draft).
 A notification (text, email) sent immediately if danger of
collision.

64. 52° North

FME Server consumes sensor data, monitors situation in real-time

Interoperable OGC interfaces for
data provision


Sensor Observation Service (SOS)
Sensor Event Service (SES)

Performs both spatial and
non-spatial analysis

Events trigger notifications, providing
situational awareness and safer
operations

65. Syncadd – USA
Daniel Riddle & Kristofor Carle
 The Mission: Monitor data uploaded via a web
interface to an Army Geospatial Data Warehouse
for compliance and data model validation,
reporting the results.
 The Solution: Use FME Server and custom
transformers to run QA tests and email the
results as Excel spreadsheets.

66. Syncadd
 Custom transformers are created and source user
parameters are published to leverage FME Server.
 Readers Used: Schema; ESRI Personal, File, & SDE
Geodatabase

67. Syncadd
Custom
transformers
complete various
tests on metadata
tags, schema
feature classes, and
schema attributes.

68. Syncadd
Results are
exported as
Microsoft Excel
spreadsheets
and emailed to
the user using
FME Server.

69. Municipality of Tuusula – Finland
Lassi Tani, Spatialworld
 The Mission: Convert environmental
observations, received as JPGs with drawn areas,
lines, and symbols, to vector data.
 The Solution: Use FME’s vectorization
transformers to produce point, line, and polygon
vector data.

70. Municipality of Tuusula
 Read JPEG files of polygon, line and point
data with separate readers.
 Change the raster data from color to
grayscale, resample, clean the rasters,
set no data, and create polygons from
the raster extents.
 Create attributes for features using
JPEG.
 Create center points for point geometry,
reproject and write points to Shape.

71. Municipality of Tuusula
 Generalize the polygon
features and build line
geometry.
 Reproject and write line
geometry to Shape.
 Clean lines and create
polygons.
 Reproject and write
polygon geometry to
Shape.

72. Municipality of Tuusula
 Final result: clean,
attributed vector data
 Key Transformers:





RasterCellValueReplacer
CenterPointReplacer
Generalizer
CenterLineReplacer
AreaBuilder

73. Swiss Federal Roads Office – Switzerland
David Reksten, Inser
 The Mission: Perform road accident analysis
based on recorded events, with variable criteria,
identifying dangerous road segments.
 The Solution: Use FME to do a “sliding window”
analysis, using linear referencing methodology
and user-defined variables.

74. Swiss Federal Roads
 Sliding window concept – look a distance from accident
location, accumulate accidents within segment, and
calculate weighted score for number and type of accident.
Linear representation of a road, which likely is not straight in the real world.
 Locate all the dangerous sectors (Black Spots) and output
as individual and aggregated segments (where they
overlap).

75. Swiss Federal Roads
 Calibrate road segments to linear reference points to acquire
maximum M-values
 User-defined criteria, sorted by M-value, merged with road
segment – sequential list of accidents along feature
 Sliding window analysis done (PythonCaller), outputs one
feature per window with statistical analysis results
 Weighted scores classify segments as dangerous (Black Spot)
 Overlapping Black Spot segments aggregated and statistics recalculated

76. Swiss Federal Roads
 Final results, visualized
using the input roads
and the dangerous
segments (Black Spots)
as a Route Event table.