April – June 2013
Ken Bragg @KenAtSafe
European Services Manager
Safe Software
Twitter Hashtag: #FMEWT
Introducing FME 2013
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.
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
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 user- navigable “scene” using CityGRID tools to view
Proposed Windpark, view from village
UVM Systems CityGRID
New Freight Train Bypass Flythrough
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.
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.”
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
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
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
The GeoInformation Group
Data collected manually in the field are processed automatically using FME
Efficient and repeatable data publication routines achieved
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
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
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
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
KDOT Aviation
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.
Ana García de Vicuña Ruiz de Argandoña !
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
Gobierno de La Rioja
Step 1: RapidEye image is read by FME, and the ExpressionEvaluator defines formulas for each band.
Distance between the sun and earth in FME Solar azimuth angle formula in FME
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.
Gobierno de La Rioja
asdf
Vegetation index image (NDVI, OSAVI and TCARI values in raster point info)
Gobierno de La Rioja
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.
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 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
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.
WhiteStar Corp
WhiteStar Corp
WhiteStar Corp
Decoding email and processing a data order
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.
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
City of Hamilton
Key Transformers
StatisticsCalculator – looks for changes/trends that need attention
WebCharter –chart display
StringConcatenator – builds URLs for Google Charting API
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
Nuclear Power Plant Modeling
“When you have an FME Hammer, every data transformation problem is a nail…”
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.
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
Sweco
Combined with geology, surface, and sampling data
Output to 3D PDF and 3D DWG
Waterford City Council - Ireland
FME Insider Article
FME Case Study Dublin Region Project Office (DRPO)
Water Web
http://cdn.safe.com/resources/case-studies/CaseStudy_WaterWeb.pdf
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
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
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
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
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
Are YOU a Trekker?
Share your FME stories with your compatriots across the galaxy!
Send them to the FME Insider –
Coming up next!
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
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
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
pragmatica inc.
Triangulation Voronoi Domains
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.
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
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.
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
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.
Syncadd
Custom transformers are created and source user
parameters are published to leverage FME Server.
Readers Used: Schema; ESRI Personal, File, & SDE
Geodatabase
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.
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.
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.
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.
Municipality of Tuusula
Municipality of Tuusula
Final result: clean, attributed vector data
Key Transformers:
RasterCellValueReplacer
CenterPointReplacer
Generalizer
CenterLineReplacer
AreaBuilder
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.
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.
Locate all the dangerous sectors (Black Spots) and output as individual and aggregated segments (where they overlap).
Linear representation of a road, which likely is not straight in the real world.
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 re-calculated
Swiss Federal Roads
Final results, visualized using the input roads and the dangerous segments (Black Spots) as a Route Event table.
