FME Around the World

CONNECT. TRANSFORM. AUTOMATE.
Lizard Island: On Location
FME Stories From Around the World

Iowa, USA
Snow Plows, ArcGIS Online, an
iPhone, and FME

Snow Plows, ArcGIS Online, an
iPhone, and FME
 Eric Abrams, Iowa Department of Transportation
 901 Snow Plows
 32 – 34 inches of snow
 9400 miles of road
 15 million gallons of brine
 120,000 tons of salt on 45
inches of snowfall in 2013
 ROI - Every dollar spent on AVL returns $6.40
 A 10% reduction of salt is $1.4 million dollars in savings

Workflow

Automatic Vehicle Location
 Invisible to driver
 Real-time flow of data
– position, status,
material usage,
conditions
 Data uploading to
Amazon cloud
 FME moves data to
Oracle Spatial for
internal usage, then to
AGOL for public
viewing

Dashcams
 Dash-mounted
iPhones send image
stream when
vehicle is in motion
 FME handles KML
generation and
upload to Windows
Azure

AGOL Public Data Access
 ArcGIS Online
keeps public
informed with
current plow status
and conditions

Dashcam Feeds
 Public can also see
what the driver is
seeing for better
awareness of road
and weather
conditions – making
winter driving safer.

Safer Winter Driving

Sharing Open Data on GitHub
with FME
La Rioja, Spain

Sharing Public Data
 Ide Rioja committed to sharing and
collaborating on public data.
 Spatial Data Sharing taken to the next level
 Creative Commons License
 Enter GitHub

Why GitHub
 GitHub is a web-based Version Control System
(VCS) which records changes to a file or set of
files over time.
 Allows:
 commit files to a public repository
 revert files back to a previous state
 review changes made over time
 see who last modified something, and more...

Why GitHub

Sharing Public Data

How does FME Help?
 Of course FME translates data from Oracle
Spatial to GeoJSON for GitHub
 But first!
 FME reads the layer list from GitHub using Python
Scripted Parameter – git pull
 And after!
 FME commits updated GeoJSON to GitHub in Shut
Down Script – git push
 Scheduled Job on FME Server

How does FME Help?

The Beauty of GeoJSON in GitHub
 GitHub supports automatic rendering of
GeoJSON repositories using Leaflet.js
 Looking ahead
 geojson.io a Chrome extension for
editing
 IDE Rioja plans open collaboration
on spatial data with GitHub
 FME can include links to image data
when writing GeoJSON (automatic
download service)

Learn More at FME User Conference
 Extended Version of this topic will be
presented at the FME International User
Conference

CONNECT. TRANSFORM. AUTOMATE.CC BY-SA 3.0 Tony Nordin
FME Server and the
Gävle Data Portal
Gävle, Sweden

FME Server and the Gävle Data
Portal
 Peter Jäderkvist, GIS
Developer & FME Certified
Professional, Community
Development Gävle
 Provides services and
centralized workspace
organization, FME usage
tracking
 Dynamic forms via client
communication with FME
Server REST API
An evolving UI: Peter recently added upload an irregular polygon to clip

Various Maps to DWG
 Most popular workspace
 Map type (5), contours,
metadata, AutoCAD version
 XML geometry + parameters
triggers FeatureReaders
 SchemaMapper, clip &
output
Example output DWG basemap

Specialty DWG Requests
 Custom workspaces
generate specialty DWG
output for other users
 Water & sewer mains for
local water company
 Power distribution grid for
local provider

3D Model to PDF, Sketchup or
DWG
 Output: Sketchup 8, 3D-PDF and DWG
 Add streets and water, yes/no
 Drape roof tops with aerial photography, yes/no
 Drape elevation model with aerial photography,
yes/no
 Add roof models if they exist, yes/no
 Some buildings don’t have heights, a parameter
decides how to treat those, e.g. “extrude by 7
meters”

1. all parameters set to yes
except for “add roof models”.
2. all parameters set to no.
3. streets water and roof models
set to yes
Example output sketchup files
1 2
3

Reprojection Services
 On-demand coordinate reprojection
 File reprojection with error checking and format conversion

FME Portal Job History
 Job details
written to history
database
 Over 2100 run
since launch
 Web app shows
usage

Linear Referencing and
Pipe Video with FME
Los Altos, CA, USA

Linear Referencing and Pipe
Video with FME
 Amanda Graf and Raymond Kinser, FME
Certified Professionals, California CAD Solutions
 Challenge: Map and share non-spatial inspection
video footage of all sewer lines for the City of Los
Altos.
 Approach: Use FME and linear referencing
methods to QA and position video, creating an
automated, repeatable process.

Data QA Issues
 No spatial coordinates or geometry
 Data inconsistencies across video data vendors
and databases
 Differences between measured pipe lengths from
the vendors and the City
 Inconsistent data entry of defect types

Data Cleanup &
Homogenization
 Filter for unwanted and bad
data
 Time stamp formatting
 Defect notation
standardization
 Match to best known good
City records
 QA for flow direction
 Catch issues for manual
intervention

Geometry Creation
 Adjust video session data for
best pipe length
 Adjust for directionality
(video with/against flow)
 Create geometry using linear
measures, chopper, and
NeighborFinder

Video Data Sharing

Results
 Easy access to data for all
 Future processing of new
observation video
automated
 City saves money on
future contracts

Tableau Dataset
Creation
Birmingham, UK

Tableau Dataset Creation
 Dami Sonoiki, FME Certified
Professional, Dotted Eyes
(Miso)
 Problem: Tableau does great
data visualization, but lacks
good mapping capabilities
 Solution: Use FME to break
down polygons

Geometry Manipulation
 Separate individual boundaries with Deaggregator
 Generalize and reduce vertices
 Deal with donuts
 Produce OGC Well Known Text values for polys

String Manipulation
 Format WKT values to
extract coordinate strings
 StringConcatenator
appends _part_number
supplied by Deaggregator
with Code_Count to
provide unique ID
PolygonPart
 PolygonPart defines Detail
for Tableau reconstruction

List Creation
 ListExploder and
ListIndexer
create x,y coords
for each polygon
 Tableau-ready
format

Address Point
Frontage
Movement
Australia

Address Point Frontage
Movement
 Rajesh Dhull, FME Certified
Professional & Senior Data
Engineer, Data Development
Asia – Pacific, Pitney Bowes
Software
 Problem: Addresses are
pinpointed by lot centroids,
but services are provided at
the street.
 Solution: Create a value-
added, dynamic geocoding
dataset with addresses located
at the front of the property. It’s (almost) always best that your taxi arrives at
the front door rather than the living room.

Requirements
 Close to 14 million address points need to be moved to a
new position – property frontage.
 The process should be robust, reliable and repeatable every
quarter.
 The process should be able to handle heavy datasets.
 The process should be able to fit in the existing processes
smoothly and should not lead to extra times or delays in
the product releases.
 Source address points in Oracle, referential data
(boundaries, streets) in MapInfo Tab files

The Approach
1. Create state-wise views in oracle as handling 14
million records in 1 process is not desirable.
2. Create single FME workspace for frontage
movement process for states with smaller
datasets.
3. Split this process in smaller manageable
processes for states with bigger datasets as FME
performance varies greatly based on the size of
the datasets.

FME Workflow Overview
Filtered,
Buffered
Roads
Lot
Boundary
Polygons
Candidates for movement Pull address centroids from Oracle
Update Oracle

Output
 14 Million
points
processed
each quarter,
automatically.

Railway Platform
Profiling
Brno, Czech Republic
Photo Credit: Roman Báča, CSmap

Railway Platform Profiling
 Rudolf Stastny, FME Certified Professional,
CSmap, s.r.o.
 Challenge: Process hundreds of railway platform
profile DXF files derived from laser scans to look
for areas outside tolerances (preventing collisions)
 Solution: Automate it with FME

Platform Profiles

Convex Hull

Point Selection

DXF Result

Telco Spatial Data Portal
United Arab Emirates

 Business Requirement: A Telecom customer
wanted a web portal for secure internal data
sharing/downloading.
 Layer and coordinate system selections needed
 Sources included imagery, vectors, and
proprietary data, updated daily by external
contractors

System Architecture
Secure access
ArcGIS
Server
Geodata
base
FME
Download
Page
FME
Server
Firewall
Users

Secure Interface

FME Server Processing
 Single workspace with Custom Transformers
 Geodatabase Reader
 Bounding Box
 create
 Clip
 Write to choice
of format and
projection

Budapest, Hungary
Laser Scanning Roads
and FME

Laser Scanning Roads and FME
 Gyula Sz. Fekete, Head of GIS Development and Data
Production, BKK Közút Zrt. (a company of the Municipality
of Budapest)
1. Management of Mobile Laser Scanning (MLS) missions
and post-processing
2. Data conversion from CAD-based data capture system
to Oracle ArcSDE GDB
3. Point cloud data analysis

MLS Mission Management
 Aim
 visualize MLS (Mobile Laser Scanning) trajectories
 locations of MLS projects
 attribute information of each scanning project – project metadata
 (scanner, driver, acquisition time, etc.)
 positions of all exposed images
 attribute information of each images comes from image header
information.
 provide a GDB where post-processing steps can be visualized and
modified on a WebGIS GUI.

Source Data
 MLS Trajectory Data
 Riegl MLS Project Log File
 Image Headers
 Post Processing Workflow Tasks

Output
 GDB with -
 Trajectory information
 Image information
 Post-processing
workflow tasks
 WebGIS GUI with
editable workflow
tasks

CAD to 3D Geodatabase
Conversion
 DGN + MDB
 Tables linked to DGN geometry

CAD to 3D Geodatabase
Conversion

Point Cloud Analysis
 Find road surface
errors based on MLS
scanned 3D point
clouds
 Generate vector data
to be used for further
spatial analysis
 Read: 3D point clouds
and parameters

Analyze & Write: Road Surface
Errors

Analyze & Write: Rugs

CONNECT. TRANSFORM. AUTOMATE.CC BY-SA Juhanson
Belgium
Comprehensive
Waterways Data QA

Comprehensive Waterways
Data QA
 Rob Vangeneugden, FME Certified Professional,
GIM nv
 Challenge: Simplify and automate a complex data
validation process for waterways authorities
 Solution: Create a Django user interface and use
FME Server to validate, manage results, and
perform database updates

The Project
 Spans multiple waterways
authorities
 3 primary workspaces
 9 embedded custom
transformers
 27 linked custom
transformers
 +/- 3000 transformers

Custom Transformers
 Uses both linked and embedded
 Each data type has a specific custom transformer that identifies
Update/Insert/Delete by comparing geometry and attributes to operational
tables:
 Terminal
 Bridge
 Bollard
 Fairway
 Berth
 Node
 Lock
 Lock Chamber

Using Parameters
 Published –
 Passed by Django application
 Format-specific (3-GML, 9-shape) and user
credentials, verify authority
 Private –
 PostGIS connections
 Schema parameters
 Output location

Database Updates
Example update database process (lock chamber)

Benefits
 Central storage
 Divided management
 Detailed validation feedback
 Summary table
 Geographic files (downloads)
 Full update history
 Easy to expand (data formats, validation rules…)
 Fast data update / Up-to-date database

Thank You!
 Questions?
 For more information:
 www.safe.com
 blog.safe.com

FME Around the World

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FME Around the World