In October 2024, the Spanish region of Valencia faced severe flooding that underscored the critical need for accessible and actionable data. This presentation will explore two innovative use cases where FME facilitated data integration and availability during the crisis. The first case demonstrates how FME was used to process and convert satellite imagery and other geospatial data into formats tailored for rapid analysis by emergency teams. The second case delves into making human mobility data—collected from mobile phone signals—accessible as source-destination matrices, offering key insights into population movements during and after the flooding. These stories highlight how FME's powerful capabilities can bridge the gap between raw data and decision-making, fostering resilience and preparedness in the face of natural disasters. Attendees will gain practical insights into how FME can support crisis management and urban planning in a changing climate.

1. Floods in Valencia:
Two FME-Powered
Stories of Data
Resilience
con terra
The Peak of Data
and AI 2025

2. 2025
The
Peak
of
Data
and
AI
Francisco
Girón Gesteira
Managing Director
con terra Spain

3. 1. 2024 Floods in Valencia
2. Data Conversion to the Rescue
3. Mobilizing Mobility Data
4. Before the Storm Returns
Agenda

4. 2024 Floods
in Valencia
Section 1

5. 2025
The
Peak
of
Data
and
AI
Worst flood ever
● Heavy rain on Oct 29th
2024
● e.g Turís:
772 mm in 24h, 185 mm in a single hour
● Water level rising up to 3m in some areas
● 190,000 people affected
● 232 deaths, 3 missing
● 232 km rails and roads needing repair
● 100.000 damaged cars
● 10.7 billion euros in estimated damage Right click and select ‘replace image’ to add an example image from your project.
Sentinel 2 – October 31st, 2024. Source: Copernicus

6. 2025
The
Peak
of
Data
and
AI

7. 2025
The
Peak
of
Data
and
AI
Worst flooding ever

8. 2025
The
Peak
of
Data
and
AI

9. 2025
The
Peak
of
Data
and
AI
Worst flooding ever

10. 2025
The
Peak
of
Data
and
AI

11. 2025
The
Peak
of
Data
and
AI
The aftermath
Source: RTVE

12. Data
Conversion to
the Rescue
Section 2

13. 2025
The
Peak
of
Data
and
AI
Unidad Militar de Emergencias (UME)
Challenges:
● Data in several different formats: JP2, composición RGB geotiff,
● Destino: carpetas con imágenes, informes de inteligencia
● Data quality: different preprocessing, corrección atmosférica,
ortorectificacion,
● Data size: óptica ~1GB, radar algo más, JPEG MBs
● Different sources: unidades militares, Airbus, SatCen,
● Composición de bandas
● Coordinación de búsqueda con bomberos, ejercito de tierra

14. 2025
The
Peak
of
Data
and
AI
Unidad Militar de Emergencias
Branch of the Spanish Armed Forces responsible for providing
disaster relief
● Forest fires
● Earthquakes
● Pandemic
● Volcanos
● Storms
● Flooding
● Black outs

15. 2025
The
Peak
of
Data
and
AI
Unidad Militar de
Emergencias (UME)
Branch of the Spanish Armed Forces responsible for
providing disaster relief:
- Forest fires
- Earthquakes
- Pandemic
- Volcanos
- Storms
- Floods
- Black outs

16. 2025
The
Peak
of
Data
and
AI
Challenges
● Loads of data coming from different sources
● Different data types
● Different data formats
● Different data quality
● Need to make this data available for several end users
● Highly isolated and secured systems
● No previous FME knowledge
● Practical rather than optimal

17. 2025
The
Peak
of
Data
and
AI
How did we do this?
FME workspace example for extraction of outliers

18. 2025
The
Peak
of
Data
and
AI
Solution
● Format conversion to widely used formats like JPG or PNG
● Quality control:
● Orthorectification, atmospheric correction, band combination, radiometric outliers
● Clipping to geographic boundary (e.g. municipality)
● Data distribution in structured shared directories

19. Thanks to FME, earth
observation images were made
available to rescue crew
operations how, where and when
they needed them

20. Mobilizing
Mobility Data
Section 3

21. 2025
The
Peak
of
Data
and
AI
Mobility data
The Spanish Transport Ministerium collects, analyses and publishes mobility data
through a web-based map application
Source:
● Anonymized mobile phone data
Goal:
● Understand travel demand and improve the transportation system by
adapting networks and services

22. 2025
The
Peak
of
Data
and
AI
Mobility data in the context of the 2024
Valencia floods
Opportunity to use this data to:
● Understand the impact on travel behavior and mobility patterns
● Prioritize infrastructure recovery and rebuilding efforts
● Identify and assess alternative routes and modes of transport
● Plan and optimize temporary or replacement transport services
Goal:
● To directly publish origin-destination matrices to support detailed analysis of
mobility flows

23. 2025
The
Peak
of
Data
and
AI
We needed a solution that
could be deployed
immediately and scaled
easily.
FME Flow Hosted was the
clear choice

24. 2025
The
Peak
of
Data
and
AI
How did we do this?
FME Flow Web App for data download

25. Self-service data download
portal built within a few hours

26. 2025
The
Peak
of
Data
and
AI

27. 2025
The
Peak
of
Data
and
AI
“I am a big fan of FME. We use it for
everything!!! Except to program the
washing machine.”
— Tania Gullón Muñoz-Repiso, R&D and geospatial analysis coordinator,
Spanish Transport Ministerium

28. Before the
Storm
Returns
Section 4

29. 2025
The
Peak
of
Data
and
AI
This is not finished
6 months after, Valencia region is still affected
The next flooding will come
Next points of action:
● Reassess flood risk maps
● Improve readiness
● Promote awareness
● Encourage data-driven preparedness

30. 2025
The
Peak
of
Data
and
AI
Recap: The Power of Data
Integration
What did we learn:
● Data when, where and how you need it
● Usability matters
● Complex things made easy

31. 2025
The
Peak
of
Data
and
AI
ThankYou
Francisco Girón Gesteira
con terra
f.giron-gesteira@conterra.es