Our Mission:To seek out innovative FME usersthroughout the galaxy, sharingtheir stories and ideas to inspireyou to take your data where nodata has gone before.
FME does 3D!Kansas DOT Division of Aviation - USA The Mission: Preserve airport usability to ensurethat air ambulance service is readily available tothe public. The Solution: Build a public online tool toillustrate and evaluate the effects of proposedvertical constructions on airport airspace
KDOT AviationThe Kansas Airspace Awareness Tool Google Earth based FME generates 3D airspace polygons using mathematicalinterpretations of verbose FAA descriptions Users place proposed vertical constructions – windmill, celltower, office building – and check for conflicts with airspaceand FAA requirements FME handles updates to respective airport and FAA data
KDOT Aviation Automatically convert human-readabledescriptions into 3D geometry,eg. “Below 7,000 ft AGL within an 8 mile radius of X.” Repeatable processes enable non-FME experts toperform data maintenance tasks Choice of KML and Google Earth creates a toolusable by anyone
FME does 3D!UVM Systems - Austria The Mission: Create CityGRID navigable 3Dworlds with thousands of individual 3D models The Solution: Automate model and terrain datapreparation and QA tasks with FME
UVM Systems CityGRID Custom transformers collect linework, orthophotos, andcreate models, and flag for manual intervention if questionsencountered (hole in roof, building footprint exceeds roofarea) FME also used to prepareterrain from ortho, pointcloud, terrain models All data combined in user-navigable “scene” usingCityGRID tools to view Proposed Windpark, view from village
UVM Systems CityGRID New Freight Train Bypass Flythrough
UVM Systems CityGRID Custom transformers bundle up repetitive tasksfor easy re-use FME slashes processing time through automationand QA CityGRID selected to process 2.7 million buildings forswisstopo UVM now plugs into customers’ data storesrapidly, regardless of platform
FME does Data Integration!San Antonio Water System – USAToni Jackson & Larry Phillips The Mission: Integrate multiple systems and datatypes across departments, while adopting a newOracle-based asset management system. The Solution: Use Esri’s FME-based DataInteroperability Extension to handle it, and savea pile of money at the same time.
San Antonio Water System“The Data Integration gave us the opportunity to correct, cleanse, reconcileand expose data that had been inaccurate. It’s also a chance for our team tobuild new workflows, validation processes and rules to ensure accurate data.”
San Antonio Water SystemEffective dataaffects all ofSAWS
San Antonio Water SystemNew developments – QA/QC streamlined – 50data integrity checks runand reported on weekly Syncing GIS and assetmanagement data viewsacross company"Without FME, we would haveneeded to double our team toaccomplish what we did with afew peoples effort. In fact, weestimate the money saved inour first year alone is nearly$1,000,000.” - 2011
FME does Automation!WhiteStar Corp - USA The Mission: Automate a manually intensive landgrid data ordering and fulfillment system forexternal customers. The Solution: Use FME Server’s email protocolsupport to process and fulfill emailed data orders– in the cloud.
WhiteStar Corp Decoding email and processing a data order
FME does Linear Referencing!Swiss Federal Roads Office – SwitzerlandDavid Reksten, Inser The Mission: Perform road accident analysisbased on recorded events, with variable criteria,identifying dangerous road segments. The Solution: Use FME to do a “sliding window”analysis, using linear referencing methodologyand user-defined variables.
Swiss Federal Roads Sliding window concept – look a distance from accidentlocation, accumulate accidents within segment, andcalculate weighted score for number and type of accident. Locate all the dangerous sectors (Black Spots) and outputas individual and aggregated segments (where theyoverlap).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 acquiremaximum M-values User-defined criteria, sorted by M-value, merged with roadsegment – sequential list of accidents along feature Sliding window analysis done (PythonCaller), outputs onefeature 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, visualizedusing the input roadsand the dangeroussegments (Black Spots)as a Route Event table.
FME does non-spatial(excel)!pragmatica inc. – JapanTakashi Iijima The Mission: Estimate radioactive materialconcentrations in agricultural water supplycatchments near Fukushima The Solution: Use FME to interpolate tabularregional observation data for catchment areas
pragmatica inc. Source data: excel of observations, cesiumconcentrations, and locations Shape irrigation catchment areas Observation points are notcoincident with catchments Create a surface model using Zfor the cesium value
pragmatica inc.Two methods required:Delaunay triangulation and linearinterpolation Uses observation points as vertices,divide catchment polygons Interpolate values at center of gravity Calculate area-weighted average ofcatchment area partsVoronoi decomposition and Tiessenmethod Use observation points as seeds Divide catchment areas by Voronoiedges Calculate area-weighted average
FME does it all!CN Railway - Canada/USAYves St-Julien The Mission: Optimize operations at NorthAmerica’s only transcontinental rail network, withover 20,000 route-miles of track. The Solution: Use FME Desktop and FME Serverto deliver automated, real time, or event-drivensolutions to almost every CN group and practice.
CN RailwayFME does LiDAR LiDAR processing extracts surface and trackfeatures to generate alignments, corridors, andslope analysis
CN RailwayFME does Real-Time! FME Server brings spatial to real time eventprocessing
CN RailwayBut 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 ServerREST services
FME does Location-Based Notifications!52° North – GermanySimon Jirka, 52° North and Christian Dahmen, con terra The Mission: To create a prototype system usingsensors to assist ships in safe passage underbridges on inland waterways. The Solution: Use FME Server to calculate andmonitor available clearance and ship height,sending notifications if danger exists.
52° NorthData 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 andclearance from water reference level
52° NorthWorkflow: When captain subscribes to the service, the ship’s AIS sendsdata to FME Server, which tracks its position. As a ship approaches a bridge, water level (from sensors) iscompared to bridge height, providing available clearance. Clearance is compared to current height above water (shipheight minus draft). A notification (text, email) sent immediately if danger ofcollision.
52° North FME Server consumes sensor data, monitors situation in real-time Interoperable OGC interfaces fordata provision Sensor Observation Service (SOS) Sensor Event Service (SES) Performs both spatial andnon-spatial analysis Events trigger notifications, providingsituational awareness and saferoperations
FME does Vector and Web!City of Hamilton Public Health Unit - CanadaShane Thombs The Mission: Automate a manual processcombining spreadsheets, databases, GIS, andstatistical analysis. The Solution: Use FME to build a reporting tool inGoogle Earth, reducing report generation timefrom one week to 12 minutes.
City of Hamilton West Nile Virus tracking uses statistical and spatialanalysis of field observations over time Geomedia® Pro, databases, and spreadsheets (forcharting) were part of manual process Replaced with FME to combine all functions andgenerates KML Reporting tool is now interactive, in Google Earth
City of HamiltonKey Transformers StatisticsCalculator – looksfor changes/trends that needattention WebCharter –chart display StringConcatenator – buildsURLs for Google Charting API
City of Hamilton Automating repetitive tasks = huge time savings,reduced reliance on single specialists/points offailure Faster report availability supports quickerdecisions on level of risk and disease controlactivities Creative transformer use opens up newpossibilities
FME does Raster!Gobierno de La Rioja – SpainAna García de Vicuña The Mission: Generate land cover classificationfrom RapidEye multispectral images foragricultural analysis – without requiredalgorithms available in remote sensing software The Solution: Use FME to do it, in a singleworkspace.
Gobierno de La Rioja Step 1 – Convert each pixel’s Digital Number (DN) to a radiancevalue by multiplying the DN by the radiometric scale factor. Step 2 – Convert radiance values to ToA (top of atmosphere)reflectance values, takinginto consideration variablessuch as: distance from the sun and angle of incoming solarradiation.Defining variables to be used in the workspace
Gobierno de La Rioja RapidEye image is read by FME, and the ExpressionEvaluatordefines formulas for each band.Distance between the sun and earth in FMESolar azimuth angle formula in FME
Gobierno de La Rioja RasterExpressionEvaluatorperforms ToA calculations ineach band. Step 3 – use anotherRasterExpressionEvaluatorto calculate vegetationindexes (NDVI, TCARI, andOSAVI). The results arewritten to TIFF.• NDVI (Normalized Difference Vegetation Index)• TCARI (Transformed Chlorophyll Absorption in Reflectance• OSAVI (Optimized Soil Adjusted Vegetation Index)
Gobierno de La Rioja asdfVegetation index image (NDVI, OSAVI and TCARI values in raster point info)
Gobierno de La Rioja Gobierno de La Rioja also generate great PDFCartographic Maps using FME
FME does non-spatial & QA/QCSyncadd – USADaniel Riddle & Kristofor Carle The Mission: Monitor data uploaded via a webinterface to an Army Geospatial Data Warehouse forcompliance and data model validation, reporting theresults. The Solution: Use FME Server and customtransformers to run QA tests and email the results asExcel spreadsheets.
Syncadd Custom transformers are created and source userparameters are published to leverage FME Server. Readers Used: Schema; ESRI Personal, File, &Enterprise Geodatabase
SyncaddCustomtransformerscomplete varioustests on metadatatags, schemafeature classes, andschema attributes.
SyncaddResults areexported asMicrosoft Excelspreadsheetsand emailed tothe user usingFME Server.
FME goes nuclear (2D -> 3D)!Sweco – SwedenUlf Månsson and Johan Sigfrid The Mission: Create a 3D model to assist indecommissioning 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 aboveground Define and attribute rooms Set wall thickness and extrude to 3D Punch out holes for rooms spanning floorsvertically Generate one-meter square grid for recordingmeasurements, inside and outside
Sweco Combinedwithgeology, surface, andsampling data Output to 3DPDF and 3DDWG
Sweco Supported logistics planning, volume estimatingfor demolition, effective visualization andpresentations Look for new ways to use vintage data
Bonus from Norway! Minecraft anyone? The first combined land/sea terrain model ofNorway! Download athttp://www.planetminecraft.com/project/south-west-norway Thanks Boele Kuipers of the Norwegian MappingAuthority/Hydrographic Service
FME does Raster -> Vector!Municipality of Tuusula – FinlandLassi Tani, Spatialworld The Mission: Convert environmentalobservations, received as JPGs with drawn areas,lines, and symbols, to vector data. The Solution: Use FME’s vectorizationtransformers to produce point, line, and polygonvector data.
Municipality of Tuusula Read JPEG files of polygon, line and pointdata with separate readers. Change the raster data from color tograyscale, resample, clean the rasters,set no data, and create polygons fromthe raster extents. Create attributes for features usingJPEG. Create center points for point geometry,reproject and write points to Shape.
Generalize the polygonfeatures and build linegeometry. Reproject and write linegeometry to Shape. Clean lines and createpolygons. Reproject and writepolygon geometry toShape.Municipality of Tuusula
Municipality of Tuusula Final result: clean,attributed vector data Key Transformers: RasterCellValueReplacer CenterPointReplacer Generalizer CenterLineReplacer AreaBuilder