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."
2. 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.
3. 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
4. KDOT Aviation
The Kansas Airspace Awareness Tool
Google Earth based
FME generates 3D airspace polygons using mathematical
interpretations of verbose FAA descriptions
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
6. KDOT Aviation
Automatically convert human-readable
descriptions into 3D geometry,
eg. “Below 7,000 ft AGL within an 8 mile radius of X.”
Repeatable processes enable non-FME experts to
perform data maintenance tasks
Choice of KML and Google Earth creates a tool
usable by anyone
7. 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
8. 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
10. UVM Systems CityGRID
Custom transformers bundle up repetitive tasks
for easy re-use
FME slashes processing time through automation
and QA
CityGRID selected to process 2.7 million buildings for
swisstopo
UVM now plugs into customers’ data stores
rapidly, regardless of platform
11. 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.
12. 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.”
14. 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
15. Gobierno de La Rioja – Spain
Ana García de Vicuña
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.
16. 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
17. Gobierno de La Rioja
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
18. Gobierno de La Rioja
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.
19. Gobierno de La Rioja
asdf
Vegetation index image (NDVI, OSAVI and TCARI values in raster point info)
20. CN Railway - Canada/USA
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.
21. CN Railway
LiDAR processing extracts surface and track
features to generate alignments, corridors, and
slope analysis
22. CN Railway
FME Server brings spatial to real time event
processing
23. 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
24. 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.
25. 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
26. 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.
27. 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
28. City of Hamilton Public Health Unit - Canada
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.
29. 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
30. City of Hamilton
Key Transformers
StatisticsCalculator – looks
for changes/trends that need
attention
WebCharter –chart display
StringConcatenator – builds
URLs for Google Charting API
31. 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
32. 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.
33. 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 and output as individual
and aggregated segments (where they overlap).
34. 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 (or not)
Overlapping segments aggregated and statistics re-calculated
35. Swiss Federal Roads
Final results, visualized
using the input roads
and the dangerous
segments as a Route
Event table.
36. 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
37. 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
38. 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
40. 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.
44. 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.
45. 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.
46. 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.
47. Municipality of Tuusula
Final result: clean,
attributed vector data
Key Transformers:
RasterCellValueReplacer
CenterPointReplacer
Generalizer
CenterLineReplacer
AreaBuilder
48. Syncadd - USA
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.
49. Syncadd
Custom transformers are created and source user
parameters are published to leverage FME Server.
Readers Used: Schema; ESRI Personal, File, & SDE
Geodatabase