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FME Around the World

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A collection of FME stories and projects from users around the globe

Published in: Technology
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FME Around the World

  1. 1. Around the World with FME
  2. 2. Multilevel Tile Cache Generation Engine LA RIOJA, SPAIN Ana García de Vicuña Pablo Martínez Gobierno de La Rioja
  3. 3. MBTiles format Store pre-cache tiles in a sqlite database to speed web mapping Implemented in the workbench
  4. 4. Mapnik - Toolkit for making high quality raster maps - Complex styles and simbology - FME transformer since FME 2014 (MapnikRasterizer)
  5. 5. Multilevel tile cache generation engine - Raster data - Vector data - Text labels - Parameters (zoom, bbox,…) MapnikRasterizer = f(zoom) Input Apply symbology WebMaptiler (GoogleMaps compatible) Tiled data
  6. 6. Multilevel tile cache generation engine Save unique tiles Optimizing MBTiles MBTiles database Mbtiles f(zoom) Output - metadata - images - maps tiles Water 60% Land 38% Mix 2% Tiles TMS Tables View
  7. 7. http://bit.ly/iderioja Multilevel tile cache generation engine
  8. 8. Ana García de Vicuña Pablo Martínez Thank you very much!!! See you in Barcelona… View the full presentation at http://goo.gl/0dqjR4 (en español)
  9. 9. 3D DGN to Multipatch for City Engine UNITED KINGDOM Konrad Poplawski
  10. 10. Thames Tideway Tunnel Needed:  a reliable, repeatable method to transfer 3D model data between Bentley MicroStation and Esri City Engine. Challenges:  no direct format support  georeferencing  attribution
  11. 11. Basics  Collada chosen as an interim format  DGN is georeferenced  Python script reads file pairs based on name  Batch deploy processes full sets of models
  12. 12. Maintaining Georeferencing  BoundingBoxAccumulator  CenterPointReplacer
  13. 13. Attribute Handling  Create attribute lists from original CAD file  Generate keys for downstream re-attribution  Clean up attributes
  14. 14. Re-attribution  asdf
  15. 15. The Result: 3D Web View  Esri Multipatch Feature Classes, positioned and with attributes  Detailed planned construction available to all stakeholders in a browser
  16. 16. Data Migration at Irish Water IRELAND Patrick Daly Irish Water
  17. 17. The Project  Irish Water formed in 2013 to bring together water and wastewater services of 34 Local Authorities  Networked data including nearly 80,000 km of pipe and hundreds of thousands of point features
  18. 18. Challenges  Source: Local authority data in CIS and MapInfo datasets  Not in consistent formats  30+ variants in format and schema Water Below Ground Selected Feature Class Feature Count Length /km Air Valve 34,479 Fittings 493,798 Flow Control Valves 2,932 Hydrants 148,451 Network Meters 28,221 System Valves 229,623 Laterals (Service/Comms) 307,003 6,293 Water Mains (of which 6,055Km Private) 713,390 61,183
  19. 19. Solving with FME  Destination: complex ArcGIS Water Utility Data Model  30+ sets of workspaces for normalization and migration  15 hours processing time
  20. 20. Destination: IW Enterprise GIS
  21. 21. Irish Water We completed this project in 9 weeks using FME, migrating almost 80,000 km of Water & Waste Water Network data. This scale of project with this timeline would not be achievable without FME. Patrick Daly, Asset Register & Data Aggregation Specialist Irish Water Irish Water GIS Migration Team Kelly Brady – GIS Analyst North West Sean Minogue – GIS Analyst East/Midlands Sandra Nestor - GIS Analyst East/Midlands Rónán O’Shea – GIS Analyst South Mark Healy – Reporting & Information Analyst
  22. 22. BIM to GIS at Mount Vernon VIRGINIA, USA Patrick Gahagan, Esri Quinn Evans Architects Mount Vernon Ladies’ Association
  23. 23. Mount Vernon  George Washington’s home, constructed in the late 1800s  Mount Vernon Ladies’ Association tasked with restoration, interpretation, and preservation of grounds and structures  Mansion laser scanned to create architectural-quality HBIM in Revit by Quinn Evans Architects
  24. 24. BIM to GIS via Data Interop  Revit: Add coordinates and rotation from project north to true north  Export to Revit Archive(.rvz) with FME Revit Exporter, attributes to spreadsheet  Import to ArcGIS with Data Interoperability Extension (FME)  Reconnect attribution
  25. 25. A Blended World
  26. 26. Viewshed Analysis  asdf
  27. 27. Stakeholder Access to Information  Browser delivery provides data to everyone, even fire suppressant system designer  Historical data identifies plaster from 1950 vs. 1787  Framing details assist with optimal routing
  28. 28. A navigable, queriable world provides minute detail and the big picture to all stakeholders – BIM/GIS pros or not.
  29. 29. OS 3D Data Generation: Using FME to Investigate the Creation of Point Clouds from Imagery UNITED KINGDOM Nikki Goodwyn Photogrammetric Surveyor Ordnance Survey
  30. 30. THE CHALLENGE  SYNCHRONISING TWO OUTPUTS: DIGITAL SURFACE MODEL (DSM) AND DIGITAL TERRAIN MODEL (DTM) DSM DTM
  31. 31. METHODS Point Cloud- No Colour ValuesRGB colouring brings points back to reality
  32. 32. SOLUTION FME WORKBENCH
  33. 33. INPUT 1: ORTHO GEOTIFF FME EXTRACTS Raster Properties Including RGB Properties Combined into RGB & Real64 FME CONVERTS DEM (XYZ) INT32 TO REAL64 INPUT 2: DSM GEOTIFF Scaled and Output as LAZ Point Cloud SOLUTION Input =1km Ortho Tile as Geotiff Input= 1km DSM of same Tile as Geotiff RasterBandInterpretation Coercer RasterPropertiesExtractor RasterExpressionEvaluator
  34. 34. RESULTS COLOURISED IMAGE-BASED POINT CLOUD OUTPUT
  35. 35. Nikki Goodwyn NGoodwyn@os.uk 02380 055972 THANK YOU
  36. 36. UAV-Based LiDAR Data Collection & Analysis FINLAND Ville Koivuranta Sharper Shape Ltd.
  37. 37. Next Eagle® by Sharper Shape  Our service fully automatically detects each vegetation issue that threatens the transmission network. We prioritize and visualize the vegetation observations to create ready management plans and work orders that customer could send to his subcontractors.  We identified 3-5 times more issues in the areas that our solution inspected than with traditional methods.  This enabled need based vegetation management.
  38. 38. UAV Solution  Sharper Shape UAV’s are capable Beyond Visual Line Of Sight flights with up to 1 hour flight time and up to 8 kg payload.  Sharper Shapes UAV’s are equipped with high performance LiDAR, High resolution cameras, onboard computer and storage unit and high precision IMU (Inertial Measurement Unit)
  39. 39. FME in Route Planning  Economically optimized flight routes are created using FME.  The elevation information is sourced from existing point cloud or from national DTM.  3D shape polylines are uploaded to autopilot and the UAV follows the pre programmed flight line.
  40. 40. Customer Data Integration  FME is perfect to fulfill customer specific small needs.  For example one customer wanted to be able to create cross-section images from network and it was done using FME  Also all data conversions from customer NIS system to our systems are done with FME.  FME is used to create PDF-map of the vegetation issues that can be used field personnel to do the vegetation clearances.
  41. 41. Prioritization Problem  Distribution companies can have hundreds of thousands of vegetation issues.  Vegetation management need to be targeted to where it is most needed.  Economically optimized vegetation clearance plans created with FME can reduce expected losses caused by power interruptions up to 3-5 times more than legacy methods.
  42. 42. Vegetation Management Prioritization Handle the areas in the priority order. Proceed with planning.
  43. 43. Conclusions  Combination of these new technologies allows us to take full control of our asset management.  Our goal is to create full turn key solution that is both cheaper and more effective than any legacy method.
  44. 44. Building a Spatial Decision Support System for Natural Gas Pipeline Risk OKLAHOMA, USA Matt Landry Frank Yeboah Williams Companies
  45. 45. Gas Gathering System R_GGS_FACTS.fmw [R_GGS_FACTS] SEGMENT_ID R_SEGID_FACTS.fmw [R_SEGID_FACTS] GISID_ID R_GISID_FACTS.fmw [R_GISID_FACTS] SUBSEG_ID R_SUBSEGID_FACTS.fmw [R_SUBSEGID_FACTS]
  46. 46. R_SUBSEGID_FACTS.fmw
  47. 47. Image Source: http://www.utc.wa.gov/publicSafety/ Documents/Williams%20Presentation %20-%20ILI%20Technologies.pdf
  48. 48. Leveraging FME Cloud: Near Real Time Global Web Map Tile Generation for Meteorology CANADA Pelmorex
  49. 49. The Project Produce web map tiles from a worldwide forecast meteorological model called ECMWF. Layers include:  Precipitation  Sea Surface Temperatures  Ground Temperatures  Wind speed and direction
  50. 50. Challenges  880,000 tiles need regenerating every 12 hours.  The maps are time sensitive so the data needs processing as quickly as possible.  Each run needs around 80 hours of compute time.  Their on-premises 10 engine FME Server did not have powerful enough hardware.
  51. 51. Solution  Leverage FME Cloud elastic processing workflows.  Provision FME capacity dynamically every 12 hours.  Leverage AWS services such as S3, SQS and AWS Lambda.
  52. 52. Solution – Dynamically Provision Capacity
  53. 53. Solution – Processing Data using SQS
  54. 54. Cost Analysis  Costs $80 per run or $58,000 annually.  On FME Server to replicate performance they would need 40 engines (~ $300,000) and a lot of hardware.
  55. 55. Oracle Spatial to SAP HANA at Alliander NETHERLANDS Stefan Koster BI&A Architect
  56. 56. Oracle Spatial to SAP HANA at Alliander
  57. 57. The Challenge  Alliander wants repeatable workflows to move data from Oracle Spatial/Esri Geo Data warehouse to SAP HANA for advanced SAP/GEO BI analysis  Departments aligning, GIS has FME already  FME preferred, complex spatial handling  Will FME do the job?
  58. 58. JDBC Technology Preview  Java Database Connectivity tech preview in FME 2015  Configured for SAP HANA  Transformers massage geometry into expected format
  59. 59. The Results
  60. 60. How did FME do?  Complete ETL process 2.5 to 3 times faster than the tested alternative  Advanced spatial handling of FME enables automation  ETL tasks across departments on a common platform
  61. 61. Geostor: State GIS Clearinghouse Cloud Migration ARKANSAS, USA Seth LeMaster Tony Davis Arkansas GIS OfficePhoto by Chriseast18/ CC BY
  62. 62. Overview Arkansas GIS Clearinghouse  GIS open data portal for the state of Arkansas  1222 monthly downloads (2,358 items downloaded)  Users  108 registered  2,387 non-registered
  63. 63. Overview
  64. 64. The Project - GeoStor Improve the usability and migrate to the cloud.  300 vector datasets migrated to PostGIS and AWS S3  3TB of raster data  4TB of historical raster data to AWS Glacier
  65. 65. Architecture
  66. 66. The Workspace
  67. 67. Benefits of Cloud  Stability – Fault tolerant data storage and Safe monitor and support FME Cloud architecture.  Security – Leverage AWS compliance and FME Cloud security policies.  Simplicity – Focus on problem not the administration  Price – 3 times cheaper than on-premises
  68. 68. Option 1 – On Site Upgrade Monthly Yearly Up-Front DIS Server Space $3,200.00 FME Dekstop $6,000.00 FME Server $12,000.00 Dell Hardware $100,000.00 SQL Server Windows Server Std (Software Assurance)[5] $595.00 Windows Sevrer Enterprise (Software Assurance) [1] $385.00 Microsoft SQL Server (Software Assurance) $270.00 Microsoft SQL Server Standard Edition (Licenses) $294.00 Microsoft Server Std Edition (License) [6] $1,662.00 Microsoft Windows Server Enterprise Ed (Licences) [5] $4,490.00 Symantec $680.00 Tape Backups (No server) $2,000.00 Total Recurring Montly Payment $3,200.00 Total Recurring Yearly Payment $26,376.00 Total One Time Cost (Every 3 yrs) $102,000.00 Total Three Year Cost $296,328.00 True Monthly Cost (/36 months) $8,231.33 Intangiable Costs Hardware Maintenance Time DIS Process Non-Scaleable
  69. 69. Option 2 – Cloud Monthly Yearly Up-Front FME Dekstop $6,000.00 FME Cloud $14,400.00 EC2 Instance (m3.large) $395.81 AWS Storage (EBS 780GB) $77.11 AWS Storage (S3 2TB) $61.30 AWS Storage (Glacier 4.2 TB) $42.41 Total Recurring Montly Payment $576.63 Total Recurring Yearly Payment $20,400.00 Total Three Year Cost $81,958.68 True Monthly Cost (/36 months) $2,276.63 Our rack space costs (real estate on our data center floor) $3,800 per month. Add to that the hardware costs, etc and you start to see why moving to the cloud was a no brainer for us. Anthony Davis, State Arkansas
  70. 70. Thank You!  Questions?  blog.safe.com

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