0
April – June 2013
Our Mission:
To seek out innovative FME users
throughout the galaxy, sharing
their stories and ideas to inspire
you to tak...
Kansas DOT Division of Aviation - USA
 The Mission: Preserve airport usability to ensure
that air ambulance service is re...
KDOT Aviation
The Kansas Airspace Awareness Tool
 Google Earth based

 FME generates 3D airspace polygons using mathemat...
KDOT Aviation
KDOT Aviation
 Automatically convert human-readable
descriptions into 3D geometry,
eg. “Below 7,000 ft AGL within an 8 mi...
UVM Systems - Austria
 The Mission: Create CityGRID navigable 3D
worlds with thousands of individual 3D models

 The Sol...
UVM Systems CityGRID
 Custom transformers collect linework, orthophotos, and
create models, and flag for manual intervent...
UVM Systems CityGRID
 New Freight Train Bypass Flythrough
UVM Systems CityGRID
 Custom transformers bundle up repetitive tasks
for easy re-use
 FME slashes processing time throug...
San Antonio Water System – USA
Toni Jackson & Larry Phillips

 The Mission: Integrate multiple systems and data
types acr...
San Antonio Water System
“The Data Integration gave us the opportunity to correct, cleanse, reconcile
and expose data that...
San Antonio Water System

Effective data
affects all of
SAWS
San Antonio Water System
New developments –
 QA/QC streamlined – 50
data integrity checks run
and reported on weekly
 Sy...
Gobierno de La Rioja – Spain
Ana García de Vicuña

 The Mission: Generate land cover classification
from RapidEye multisp...
Gobierno de La Rioja


Step 1 – Convert each pixel’s Digital Number (DN) to a radiance
value by multiplying the DN by the...
Gobierno de La Rioja


RapidEye image is read by FME, and the ExpressionEvaluator
defines formulas for each band.

Solar ...
Gobierno de La Rioja

 RasterExpressionEvaluator
performs ToA calculations in
each band.

 Step 3 – use another
RasterEx...
Gobierno de La Rioja
 asdf

Vegetation index image (NDVI, OSAVI and TCARI values in raster point info)
CN Railway - Canada/USA
 The Mission: Optimize operations at North
America’s only transcontinental rail network, with
ove...
CN Railway
 LiDAR processing extracts surface and track
features to generate alignments, corridors, and
slope analysis
CN Railway
 FME Server brings spatial to real time event
processing
CN Railway
But wait, there’s more!
 Grid > polygon cellular coverage analysis
 SQL Server decommissioning to Oracle Spat...
52° North – Germany

Simon Jirka, 52° North and Christian Dahmen, con terra

 The Mission: To create a prototype system u...
52° North
Data Sources:
 Onboard Ships: Automated Identification System
(AIS) send Ship ID, position, course, speed, heig...
52° North
Workflow:
 When captain subscribes to the service, the ship’s AIS sends
data to FME Server, which tracks its po...
52° North


FME Server consumes sensor data, monitors situation in real-time



Interoperable OGC interfaces for
data pr...
City of Hamilton Public Health Unit - Canada
 The Mission: Automate a manual process
combining spreadsheets, databases, G...
City of Hamilton
 West Nile Virus tracking uses statistical and spatial
analysis of field observations over time
 Geomed...
City of Hamilton
Key Transformers
 StatisticsCalculator – looks
for changes/trends that need
attention

 WebCharter –cha...
City of Hamilton
 Automating repetitive tasks = huge time savings,
reduced reliance on single specialists/points of
failu...
Swiss Federal Roads Office – Switzerland
David Reksten, Inser

 The Mission: Perform road accident analysis
based on reco...
Swiss Federal Roads
 Sliding window concept – look a distance from accident
location, accumulate accidents within segment...
Swiss Federal Roads
 Calibrate road segments to linear reference points to acquire
maximum M-values
 User-defined criter...
Swiss Federal Roads
 Final results, visualized
using the input roads
and the dangerous
segments as a Route
Event table.
pragmatica inc. – Japan
Takashi Iijima

 The Mission: Estimate radioactive material
concentrations in agricultural water ...
pragmatica inc.
 Source data:
 excel of observations, cesium
concentrations, and locations
 Shape irrigation catchment ...
pragmatica inc.
Two methods required:
Delaunay triangulation and linear
interpolation
 Uses observation points as vertice...
pragmatica inc.
Triangulation

Voronoi Domains
WhiteStar Corp - USA
 The Mission: Automate a manually intensive land
grid data ordering and fulfillment system for
exter...
WhiteStar Corp
WhiteStar Corp
WhiteStar Corp
 Decoding email and processing a data order
Municipality of Tuusula – Finland
Lassi Tani, Spatialworld

 The Mission: Convert environmental
observations, received as...
Municipality of Tuusula
 Read JPEG files of polygon, line and point
data with separate readers.
 Change the raster data ...
Municipality of Tuusula
 Generalize the polygon
features and build line
geometry.
 Reproject and write line
geometry to ...
Municipality of Tuusula
 Final result: clean,
attributed vector data
 Key Transformers:






RasterCellValueReplac...
Syncadd - USA
 The Mission: Monitor data uploaded via a web
interface to an Army Geospatial Data Warehouse
for compliance...
Syncadd
 Custom transformers are created and source user
parameters are published to leverage FME Server.

 Readers Used...
Syncadd

Custom
transformers
complete various
tests on metadata
tags, schema
feature classes, and
schema attributes.
Syncadd
Results are
exported as
Microsoft Excel
spreadsheets
and emailed to
the user using
FME Server.
Are YOU a Trekker?
Share your FME stories with your
compatriots across the galaxy!

Send them to the FME Insider –
fmeinsi...
Coming up next!
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FME Around the World (FME Trek, Part 2): Ciaran Kirk - Safe Software FME World Tour 2013

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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."

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Transcript of "FME Around the World (FME Trek, Part 2): Ciaran Kirk - Safe Software FME World Tour 2013"

  1. 1. April – June 2013
  2. 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. 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. 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
  5. 5. KDOT Aviation
  6. 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. 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. 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
  9. 9. UVM Systems CityGRID  New Freight Train Bypass Flythrough
  10. 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. 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. 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.”
  13. 13. San Antonio Water System Effective data affects all of SAWS
  14. 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. 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. 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. 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. 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. 19. Gobierno de La Rioja  asdf Vegetation index image (NDVI, OSAVI and TCARI values in raster point info)
  20. 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. 21. CN Railway  LiDAR processing extracts surface and track features to generate alignments, corridors, and slope analysis
  22. 22. CN Railway  FME Server brings spatial to real time event processing
  23. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 35. Swiss Federal Roads  Final results, visualized using the input roads and the dangerous segments as a Route Event table.
  36. 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. 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. 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
  39. 39. pragmatica inc. Triangulation Voronoi Domains
  40. 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.
  41. 41. WhiteStar Corp
  42. 42. WhiteStar Corp
  43. 43. WhiteStar Corp  Decoding email and processing a data order
  44. 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. 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. 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. 47. Municipality of Tuusula  Final result: clean, attributed vector data  Key Transformers:      RasterCellValueReplacer CenterPointReplacer Generalizer CenterLineReplacer AreaBuilder
  48. 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. 49. Syncadd  Custom transformers are created and source user parameters are published to leverage FME Server.  Readers Used: Schema; ESRI Personal, File, & SDE Geodatabase
  50. 50. Syncadd Custom transformers complete various tests on metadata tags, schema feature classes, and schema attributes.
  51. 51. Syncadd Results are exported as Microsoft Excel spreadsheets and emailed to the user using FME Server.
  52. 52. Are YOU a Trekker? Share your FME stories with your compatriots across the galaxy! Send them to the FME Insider – fmeinsider@safe.com
  53. 53. Coming up next!
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