Disaster response involves rapid integration of diverse data sources in order to derive spatial information critical to an effective disaster response. This presentation will look at a range of use cases where FME has been used in both disaster response scenarios and practical applications. Safe participated in a recent OGC Testbed that involved a flood disaster response scenario for the San Francisco Bay area. A number of national mapping and environment agencies use FME for managing their flood data, weather data and disaster response resources. Typical disaster response workflows and formats will also be reviewed, including how FME works with DEMs, imagery, time series data and live web feeds to integrate real time with base map data and provide the results planners need to respond effectively. FME's strong support for open standards related to emergency response will also be discussed.

1. FME and Disaster Response
Dean Hintz

2. Agenda
•Disaster response
Challenge
•FME capabilities
•Disaster management
cycle
•Data integration
approaches
•Key workflows & examples

3. Disaster Challenge
• Diversity of data types required.
– Open / proprietary
– Spatial / non-spatial
• Limitations on IT infrastructure –
bandwidth, expertise
• Urgency – compressed timelines
• Data processing and distribution
Wikipedia – us navy photo

4. Opportunities
•Common data models,
spatial reference
•Open standards
•Cloud based,
decentralized
•Mobile devices
•Crowd sourcing
•APIs, loosely coupled
•Web sources – leave in
place
•Real time, non-spatial
•Model based, rapid
prototyping
•Easy updates

5. FME meets the
Disaster challenge
• Ultimate data integration tool –
pull in data from anywhere
• Bridge the gap between
proprietary and open standards
• Access from and publish to web
• Rapid prototyping
• Automate, real-time, scalable
• Easy distribution, client friendly

6. FME Capabilities
•Formats R&W
•Web Access, Publish
•Processing Geometry:
Vector, raster, point clouds
•Processing records: ETL,
schema mapping

7. Plan
Mitigate
Respond
Recover
Disaster
Management Cycle
Integration

8. Disaster
Management Cycle
•Planning
–Hazard Identification
–Preparedness
–Mitigation
•Response
–Evolving situational awareness
–Integration
•Recovery (resilience)

9. Key Workflows:
Hazard Mapping
•Base map
•Population / settlement
•Infrastructure, transportation
•Hazard sources and extents
estimations
•Identify critical infrastructure

10. FEMA DFIRM
•FME used to track, QC, and process
data for FEMA's National Flood
Hazard Layer – flood insurance etc
•Continuing to increase the level of
automation and reduce the amount of
human input required in the process

11. Australian Bush Fires
In 2009, the Black Saturday bushfires claimed 173 lives
and over 2,000 properties.

12. Igniting the BAL Plan
•Bushfire Attack Level (BAL)
indicates fire risk
•provide assessment on location
•safest location to build
•Uses vegetation, topography,
property boundaries
•FME Cloud accessed via Ipad app

13. Forest Fire Risk Assessment:
Alpine Shire Council

14. Forest Fire Risk Assessment:
Alpine Shire Council

15. Hazard Assessment:
con terra GeoRisk Analyzer

16. Geo Risk Analyzer:
Natural Hazards

17. Hazard Summary Reports

18. Key Workflows:
Impact Assessment
•Actual disaster impact extents
•Real time updates from the field,
often non-spatial (messages,
spreadsheets)
•Map extents in 4D
•Intersect impact zones with base
map to determine severity

19. Damage Assessment: UAVs
•Rapid initial damage
assessment critical to
response: human and
infrastructure impact
•Guides level of response
•Easy to deploy UAVs
provide an ideal platform

20. UAV: Storm Damage Assessment

21. RCMSAR

22. RCMSAR: Marine
Search and Rescue
Evaluation of UAV potential for SAR
Search large areas quickly
Near shore areas can be hard to access
from land or sea.
UAV’s offer real promise to fill this gap

23. SAR: Locating Survivors
•RCMSAR – marine search and rescue
•Used FME to generate search grid
waypoints
•UAV flies grid autonomously - reduces
human error and maximizes coverage for
limited flight times
•FME used to assemble results into
photomosaic

24. Plan - Route Generation:
Search & Rescue Grids

25. Plan - Route Generation:
Parameters

26. Plan - Route Generation:
Mission CSV for DJI API

27. Plan - Route Generation:
SAR Mission Preview

31. UAS Advantage
● Safety: keeps workers on the
ground, away from hazards
● Cost effective, energy efficient
● Less impact: emissions, noise
Southern Company

32. Assess Storm DamageI85 Bridge Collapse - change picture
Assess Storm Damage

33. Assess Storm DamageI85 Bridge Collapse - change picture
I85 Atlanta Bridge Collapse

34. Southern Company:
Missions & collected visualized in
Dashboard using Portal

35. Challenge Number 2 -
AGOL to Portal using FME Cloud
… mission data delivered as a feature layer by FME rendered identically using FME
Python Caller and standard ESRI layer file. A web map is created in Portal, every picture
is a point feature on the map and is hyperlinked for review purposes

36. ATCO: Ft McMurray
Wild Fire
•FME Server / Arc
Server integration
•FME spatialized
outage spreadsheets
to geodb
•See ATCO: Scaling up
GIS Capabilities to
Support Disaster
Response By DarrenRD

37. Outage Maps Data Flow

38. Key Workflows:
Data Integration
•Connection
•Reprojection
•Interpretation
•Data model transform (schema
mapping)
•Validation
•Synthesis, Extrapolation

39. EU INSPIRE: Related Themes
•Addresses
•Administrative Areas
•Networks: Pipelines,
electricity
•Transportation
•Hydrography
•Elevation
•Natural Risk Zones
•Environmental
monitoring
•Land use
•Land cover
•Ortho imagery
•Buildings
Data Integration Example

40. Integration: EU INSPIRE
•Common, open data standard
•OGC open standards based
data and services
•Multi agency trans-national
•OGC: GML, WFS, WMS, WCS,
GeoTIFF

41. Key Workflows: Notification
•911
•Earthquake
•Weather - Lightning

42. Flood Notification
Scenario
Part 1: Retrieve Customer Data
Part 2: Generate a Flood Polygon
Part 3a: Determine Who is Affected
Part 3b: Determine Who is Affected / Create Customers
Part 4: Twilio Custom Transformer

43. Flood Notification Generator
Workspace

44. JSON Web Services

45. UK EA Flood Monitoring

46. FME: Automated JSON
Message Parsing
2. { "items":[ {
"@id":"http://environment.data.gov.uk/flood-monitoring/id/floods/99528" ,
"description":"River Coln and its tributaries" , …
"floodArea":{
"@id":"http://environment.data.gov.uk/flood-monitoring/id/floodAreas/061WAF06Coln" ,
"county":"Gloucestershire" ,
"polygon":"http://environment.data.gov.uk/flood-monitoring/id/floodAreas/061WAF06Coln/polygon" ,
"riverOrSea":"River Coln, Dudgrove Brook“ }
1. https://environment.data.gov.uk/flood-monitoring/id/floods?min-severity=4
3. http://environment.data.gov.uk/flood-monitoring/id/floodAreas/061WAF06Coln/polygon
4. {type: "FeatureCollection",
features: [{ type: "Feature",
properties: {REGION: "South East Region",
AREA: "West Thames",
FWD_TACODE: "061WAF06Coln",...},
geometry: {type: "MultiPolygon",
coordinates: [[[[-1.758934661685646,51.68604258983194],
[-1.758826005513945,51.68598898294504],
[-1.758905679948403,51.68588139134682],…

47. UK EA Flood Monitoring

48. UK EA Flood Monitoring
FME reads flood service and generates KML view

49. Real-Time
Earthquake Reports
•Powered by FME
Notification Services
•trigger workflows to run
when new data becomes
available
•serve up custom reports
automatically by email
•FME cloud based
•Stephanie Halpin

50. Key Workflows: Relief,
Recovery, Rebuilding
•Initial response
•Damage Assessment
•Relief centres, distribution
•Recovery strategies
•Reconstruction projects
•Resilience ties in with
mitigating the next disaster

51. 2011 Christchurch
Earthquake
•Stronger Christchurch Infrastructure
Rebuild Team (SCIRT)
•LINZ / Canterbury SDI
•Locus

54. LINZ Office: FME WT 2013

55. Key Resilience Workflow:
Scenario Analysis
•Modelling / simulation
•Scenarios stress / test response systems
•Best if multi-agency, with evolving impacts
over time
•1 stressor insufficient (500 year flood)
Combinations generate cumulative impacts
•Downscaling / upscaling

56. Key Workflow: NetCDF
•Weather & climate modelling
•Multidimensional arrays,
n-band raster grids in FME
•Use weather / climate sim output
to load flood models
•Output from flood model loads
impact & cost models

57. OGC TestBed 11
Open Geospatial Consortium

58. Testbed 11 Scenario
Sacramento, CA - Great Flood of 1862
San Francisco with 2.5 meter rise in sea level
Highway 1 – Pacifica, CA – Dec 2014
Dec 2014 Pacific Storm impacting San Francisco Bay

59. 1. Read NetCDF
2. Read DEM
3. Parse time step
4. Spatial transform grid to LL84
5. Raster difference Flood grid =
DEM - flood level
6. Convert flooded cells to areas
7. Format attributes & style for KML
writer
NetCDF to
GeoTIFF / KML

60. NetCDF to
GeoTIFF / KML

61. OGC TestBed 11: SFO Flood Sim

62. OGC TestBed 11: SFO Flood Sim

63. OGC TestBed 11: SFO Flood Sim

64. OGC TestBed 11: SFO Flood Sim

65. OGC TestBed 11: SFO Flood Sim

66. OGC TestBed 11: SFO Flood Sim

67. OGC TestBed 11: SFO Flood Sim

69. Flood WPS Request
<gml:FeatureCollection xmlns:tb11=http://www.safe.com/gml/fme>
<gml:featureMember>
<tb11:floodRisk gml:id="id582e787d-e61e-4692-83a6-79641d99d151“>
<gml:pointProperty>
<gml:Point gml:id="id-4f6bc043-e95e-4e54-be03-e207fa43ba0d-0"
srsName="epsg:4326" srsDimension="2">
<gml:pos>-122.364 37.6149</gml:pos>
</gml:Point>
</gml:pointProperty>
</tb11:floodRisk>
</gml:featureMember>
</gml:FeatureCollection>

70. Flood WPS
<gml:FeatureCollection xmlns:tb11=http://www.safe.com/gml/fme>
<gml:featureMember>
<tb11:floodRisk gml:id="id582e787d-e61e-4692-83a6-79641d99d151"
floodLevel="9.3129701614379883">
<gml:pointProperty>
<gml:Point gml:id="id-4f6bc043-e95e-4e54-be03-e207fa43ba0d-0"
srsName="epsg:4326" srsDimension="2">
<gml:pos>-122.364 37.6149</gml:pos>
</gml:Point>
</gml:pointProperty>
<tb11:floodLevelMax>9.3129701614379883</tb11:floodLevelMax>
<gml:description>FloodLevel=9.3129701614379883</gml:description>
</tb11:floodRisk>
</gml:featureMember>
</gml:FeatureCollection>

71. Other UC Talks
•Wildfire Notification Magic with
FME Server, Victor Rutherford
•NG9-1-1 and GIS at St. Louis
County, Missouri, Lauren Voelker
•Cumulative Risk Management –
How FME Automatically Indicates
Hail and Other Hazards out of
Data, Mark Doering – GeoRisk
Analyzer

72. Other Examples
•London Fire Birgade: routing
•TNRIS (Texas Natural Resources
Information System) Geospatial
Emergency Management Support
System
•Colorado Emergency Management

73. Key Recommendations
•Compose a Plan
•Avoid single points of
failure: eg.1 server, 1
communications
medium, 1 centre
•Establish roles and
responsibilities
•Establish connections
with stakeholders
ahead of time
•Run realistic, multi-
agency scenarios,
simulations to test
resilience

74. Summary
•Rapid generation of
the disaster picture is
key
•FME was born to
integrate: supports all
aspects of disaster
management cycle
•Support for standards
but flexible
•Web services are great
but offline is a must
•Easy to use tools:
expertise at a premium
•Automation!

75. Questions to Consider
What are some FME workflows you
have used to manage disaster data?
How can FME better support
disaster response?

76. THANK YOU!
safe.com | dean@safe.com