Presentation BDVA (Ana Garcia) at the Copernicus and AI event - January 28 2020, Brussels

1. Towards a European
AI, Data and Robotics
Partnership
Ana García Robles
Secretary General BDVA
@BDVA_PPP / @RoblesAG
“Copernicus and Artificial Intelligence” workshop
28 January 2020
Brussels, Belgium

2. “Copernicus and Artificial Intelligence”
28 January 2020
2014 2020+
42 running projects (beginning 2019)
In 2018:
• Over 1,6B€ Private investments mobilized
• 132 innovations of exploitable value by 2018
• 224 experiments/use cases (+108 from BDVA i-Spaces)
• 80 large scale experiments using closed data involving many different data types
• 106,73 Petabytes of data shared for experimentation
Towards a European AI, Data and Robotics Partnership
EuroHPC Joint Undertaking (PPP)
Shaping Industrial applications
Launch of the Big
Data Value PPP
PPP projects start 2017
Big Data Value PPP

3. 209 Members
33 Large companies
55 SMEs
106 Research institutions
15 Others
Present in 28 countries
Industry-driven and fully self-
financed international non–for-profit
organisation under Belgian law
33
16%
106
51%
55
26%
15
7%
ALL MEMBERS
Large Research SME Others

4. FULL MEMBERS: INDUSTRY

5. FULL MEMBERS: RESEARCH/ACADEMIA/Others

6. ASSOCIATE MEMBERS: INDUSTRY

7. ASSOCIATE MEMBERS: RESEARCH/ACADEMIA/Others

8. Big Data Value Reference Model

9. TF3:
Ecosystem
TF1:
Programme
TF2:
Impact
TF4:
Communication
TF5:
Policy &
Societal
Policy &
Societal
TF6:
Technical
Data Science/AI
Data Technology
architectures
HPC-Big Data
TF6-SG4: Data Protection and
Pseudonymisation
Mechanisms
TF6-SG6:
Standardisation
TF6-SG7: Data
Benchmarking
TF7:
Application
TF7-SG2: Telecom
TF7-SG3: Healthcare
TF7-SG4: Media
TF7-SG5: Earth observation
& geospatial
TF7-SG6: Smart Manufacturing
Industry
TF7-SG7: Mobility and Logistics
TF7-SG8: Smart Governance
and Smart Cities
TF7-SG9: Agri
TF7-SG10 Finance
TF8:
Business
TF8-SG1: Data
entrepreneurs
(SMEs and
startups)
TF8-SG2:
Transforming
traditional business
(Large Enterprise)
TF8-SG3:
Observatory on
Data Business
Models
TF9:
Skills and
Education
TF9.SG1: Skill
requirements
from European
industries
TF9SG2: Analysis of
current curricula
related to data
science
TF9.SG3: Liaison
with existing
educational
projects
BDVA Task Forces
TF1.SG1
SRIA
TF1.SG4
i-Spaces
TF1.SG6
WP
Tracking
TF3.SG1
Members
TF3.SG2
Collabs

10. Big Data Technologies in
Healthcare
Needs, opportunities and challenges
TF7 Healthcare subgroup
12/21/2016
BIG DATA CHALLENGES
IN SMART MANUFACTURING
A Discussion Paper on Big Data challenges
for BDVA and EFFRA Research & Innovation
roadmaps alignment
www.bdva.eu
Version 1
2018
BIG DATA CHALLENGES
IN SMART MANUFACTURING
A Discussion Paper on Big Data challenges
for BDVA and EFFRA Research & Innovation
roadmaps alignment
www.bdva.eu
Version 1
2018
THE TECHNOLOGY STACKS OF HIGH
PERFORMANCE COMPUTING AND
BIG DATA COMPUTING:
What they can learn from each other

11. BDVA i-Spaces
Federating
Experimentation
and Testing
facilities

12. Open collaboration
with different stakeholders across
Europe established
The European AI, Data and Robotics Partnership

13. The journey…
6th
June
Joint AI Vision
Paper
March 2019
Joint SRIDA
May 2019
MoU Signed
December
2018
European
Commission
Communication on AI
December 2018
& April 2019
Public Event
Brussels
18th
Sep-
temb
er
Joint SRIDA
Sep 2019
EBDVF
2019
Partnership
Proposal
Session: The
EU's Copernicus
programme: an
opportunity for
AI development
Preparing 1st official
SRIDA version
2018 2019 2020

14. The Vision of the Partnership is to boost
European competitiveness, societal wellbeing
and environmental aspects to lead the world
in researching, developing and deploying
value-driven trustworthy AI, Data and
Robotics based on European fundamental
rights, principles and values.

15. Adoption challenges: Open collaboration needed!
Standards Testing
Research Landscape
EU public-private investment
environment
Complexity of AI in Industry
and Public domain
Complex
Technological Barriers
Access to AI / Data
Infrastructure
Digital Single Market
Societal Trust in AI
AI Policy and Regulation
Skills and Know-How
AI Research
communities and
initiatives
Horizonal cooperation with
other technical PPPs

16. AI Value Chain

17. AI PPP
WA2:
Skills &
Acceptance
Build a strong AI Skill Pipeline
Understand requirement
Promote career path
Engage with Citizens
Promote Diversity
WA3:
Innovation
&
Market
Enablers
Stimulate industrial investments
Aligning with end users
Monitor Innovation
Promote experimentation
Connect to infrastructure
Connect to finance
WA4:
Guiding
Standards &
Regulation
Build trust in AI and create a level
market
Promote standards
Engage with regulators
Promote sandboxes
Promote guidelines
Communicate with policymakers
WA5:
Promoting
Research
Excellence
Boost Academia-Industry
collaborations
Jointly Implement the SRIDA
Promote Collaboration
Promote Excellence
Align Industry & Research
WA1:
Mobilising
the
European AI
Ecosystem
Join Forces
Research Communities
Horizontal Partnerships
Vertical Partnerships
Regional, National &
European Initiatives
Open and
Inclusive

18. Holistic approach needed!

19. Smart communication is a key technology for AI. Distributed AI, multiservice and Edge computing. AI for
future cost-effective communication systems and networks.
AI and HPC by nature synergetic. HPC for AI where faster decision-making is crucial and extremely complex
data sets are involved. AI for HPC.
Cybersecurity is a critical enabler for AI. Technical robustness, resiliency, dependability, safety, security, and
trust. AI for Cybersecurity.
Seamless integration of IoT technology (such as sensor integration, field data collection, Cloud, edge and fog
computing) with AI, Data and Robotics technology. Jointly building IoT-enabled Data Marketplaces.
Combination of Nano-electronics, Embedded Intelligence and Smart Systems Integration together with AI,
Data and Robotics is central to continued digitalization.
New class of self-learning, self-optimising and self-adapting systems will create the need for novels ways of
software and system development.
Machine Vision technology. Vision components major source to generate data and knowledge about
the environment and basis for decision making and control.
…… Other European players ….
Relevant initiatives contributed to the SRIDA document
(Summer 2019 – included in 2nd draft/consultation version)

20. Collaboration with AI Research communities and
initiatives being developed
Other cooperations being established
• With Standardisation Bodies
• With industry associations
• With member states / National initiatives
• With vertical Partnerships
• With DIHs and DIH networks
• …..

21. Input from some of the BDVA
members and Projects
“Copernicus and Artificial Intelligence”
28 January 2020

22. 1/27/20 Copyright © TERRASIGNA 2020 22
Hugin was extensively validated on a variety of EO
datasets (training and validation data from Romania).
The goal of the ML4EO project was to enable the easy uptake and
proliferation of state of the art ML methods in the EO community.
Result: development of a Machine Learning experimental tool for Earth
Observation – Hugin:
Design goals can be summarized as such:
• Easy configuration scheme which does not require extensive ML
background.
• Simple data ingestion mechanism which can handle large datasets for
out-of-core ML solutions
• Comprehensive processing pipeline which includes; pre-processing
methods, a comprehensive set of state-of-the-art ML and DL algorithms,
training, validation and reporting mechanisms. Pipeline extensible with
your own routines
• Provide an easy method for predictive model instantiation and the
ability to export the resulting predictive models in a widely usable format.
Forestry segmentation using S2 and CORINE data:
Identify forested areas using an Deep Learning model
(U-Net)
Transfer learning from SEN12MS (S2 only)
Input: RGB and NIR
Output: Class ID’s for each pixel (GeoJSON)
Development of open source ML tools for EO

23. Flows and expected outcomes
AGRICULTURE
(13 pilots)
FORESTRY
(8 pilots)
FISHERY
(6 pilots)
Big Data Sources
and Big Data Types
Structured and unstructured data
Spatio-temporal data
Machine generated data
Image/sensor data
Geospatial data
Genomics data
Data
Management
Collection
Preparation
Curation
Linking
Access
Data
Processing
Batch
Interactive
Streaming
Real-time
Data Analytics
Classification
Clustering
Regression
Deep learning
Optimization
Simulation
Raw material production
for Food and Energy
Biomaterials
Responsible
production
Sustainability
Data Visualization and User Interaction
1D, 2D, 3D + temporal
Virtual and Augmented Reality
Validation

24. Overall achievements
27diverse pilots (more on them later)
95technology components
(60 used in trials), 38datasets,
15pipelines
Lead project in defining the
BDVA Reference Model
DataBioHub cataloguing
DataBio book (under preparation)
180+events
4360LinkedIn members,
611Twitter followers
31exploitable results
Customized business plans
2017 2018 2019
Agriculture Pilot
Forestry Pilot
Fisheries Pilot
DataBio Platform with Pilot
Support
Earth Observation and
GeoSpatial Data and Services
Dissemination and Training
Exploitation and Business
Planning
Specified
pilots
Developed
components
and platform
Executed
pilots
trial 1
Final
docume
ntation
v2
Dissemination and Training
Exploitation and Business Planning
Executing
pilots
trial 2
Final
report

25. 1/27/20 Copyright © TERRASIGNA 2015 25
Under the framework of DataBio project, Terrasigna developed a
CAP support monitoring service for 3 areas of interest in Romania.
The general methodology is based on the comparison between
real crop behavior and the expected trends for each crop
typology.
Input data:
• EO data ingested as satellite images time series (SITS):
Copernicus Sentinel-2 & LANDSAT
• Declared crop types (following standard classification index)
• Using data mining and machine learning techniques, observed
crops maps and compliance maps of discrete levels of trust are
obtained.
The level of trust can be expressed at pixel or parcel level. Low
level of trust means that, from the algorithm’s point of view, the
current location (pixel or parcel) was cultivated with a different
crop then the one declared. A high level of trust means that the
observed crop matches the declared one.
This project is part of BDV PPP.
Employment of ML techniques for EO agriculture monitoring

26. 1/28/20 Copyright © TERRASIGNA 2020 26
Observed crop types map Classification confidence index Crop compliance map
S2 natural colors composite(27.04.2018) S2 natural colors composite (31.07.2018) S2 natural colors composite (27.09.2018)

27. © 2019 GMV Property – All rights reserved.
Burnt forest areas are mapped out using machine learning algorithm with Sentinel-2 imagery.
The analysis pre-fire/post-fire allows estimating burnt severity to support recovery plans.
AI/Big Data: enabler for Copernicus uptake
Images courtesy of H2020 MySustainbleForest. MySustainableForest issupported by the European Commission under contract No. 774652

28. Combing data and AI in Robotics to improve performance
Multi-robot Command, Control & Intelligence (C2I)
• Data intensive
• Goal decomposition – semantic and reason based
• Task planning - symbolic planning
• Path Planning – Optimal path to reach desired goals
• H2020 projects Icarus and Enduruns
AI based Mission Planning Systems (Terrestrial)
ML supporting Deterministic/Analytical methods
• Determining orientation applying auto tuning of parameters
for producing usable depth images in varying lighting
conditions
• Machine learning applied for finding optimal parameters
• H2020 project Infuse
ML applied to Perception (Space)
Research and develop of innovative systems, solutions, products and
services for the aerospace, security markets & related industries.
www.spaceapplications.com

29. Future development and directions of a strategic
collaboration
• Strategic contribution to the European Green Deal
• Support uptake of sectors linked to the Bio-Economy (not only)
• Investment in further research of AI (applied existing architecture or
development of new hybrid) applied to EO problems, such as classification,
detection, indexing, prediction, data fusion etc.
• Development of a portfolio of use cases employing AI technologies that brings
together in collaboration EO users and providers of infrastructure, of specific AI
and EO technologies and services.
• Considering the interest/focus of BDVA in Big Data and AI, the new partnership
and the interests of ESA and EC/Copernicus of applied AI in Earth observation, the
BDVA subgroup on EO shall develop a more active collaborative relationship/
project-based between the EO and the AI/Big Data communities, and naturally
extend activities of this subgroup into the new partnership.
“Copernicus and Artificial Intelligence”
28 January 2020

30. Data-driven SustAInable Society
BDVA is open to new members
Join us!!!
Info@core.bdva.eu
www.bdva.eu/get-involved
Thanks!!

31. Ana García Robles
Secretary General
Big Data Value Association
info@core.bdva.eu
@BDVA_PPP