Presentation in Data Week 2021. The main objective of this workshop is to bring together four pioneer H2020 projects that are at the frontier of European research and innovation and are developing Artificial Intelligence and Big Data technologies for Copernicus data. These projects are ExtremeEarth (http://earthanalytics.eu/), AI4Copernicus (https://ai4copernicus-project.eu/), DeepCube (https://deepcube-h2020.eu/) and CALLISTO (https://callisto-h2020.eu/). The first two of the projects have been funded by ICT calls while the other two have been funded by DT-SPACE calls.

1. May 26, 2021
Data Week 2021
Manolis Koubarakis
National and Kapodistrian University of Athens
The ExtremeEarth Data Science Pipeline for Linked
Earth Observation Data

2. 2
Not Just for ExtremeEarth
• Previous projects:
o TELEIOS
o LEO
o MELODIES
• See paper:
o Manolis Koubarakis, Kostis Kyzirakos, Charalampos Nikolaou, George Garbis, Konstantina Bereta, Roi
Dogani, Stella Giannakopoulou, Panayiotis Smeros, Dimitrianos Savva, George Stamoulis, Giannis
Vlachopoulos, Stefan Manegold, Charalampos Kontoes, Themistocles Herekakis, Ioannis Papoutsis,
and Dimitrios Michail. Managing Big, Linked, and Open Earth-Observation Data Using the
TELEIOS/LEO software stack. IEEE Geoscience and Remote Sensing Magazine, Vol. 4, Issue 3,
pages 23-37, September 2016.

3. 3
The ExtremeEarth Project (From Copernicus Big
Data to Extreme Earth Analytics)
• Funded under the H2020 call ICT-12-2018-2020: “Big Data technologies and extreme-scale analytics”
• The project started in January 2019 and finishes in December 2021
• See http://earthanalytics.eu/

4. 4
ExtremeEarth Main Objective
• The main objective of ExtremeEarth is to develop Artificial Intelligence and Big Data techniques and
technologies that scale to the PBs of big Copernicus data, information and knowledge, and apply these
technologies in two of the ESA Thematic Exploitation Platforms: Food Security and Polar.
• The technologies to be developed will extend the European Hopsworks data intensive AI platform of
partner Logical Clocks to offer unprecedented scalability to extreme data volumes and scale-out
distributed deep learning for Copernicus data.
• The extended Hopsworks platform will run on CREODIAS and will be available as open source to enable
its adoption by the strong European Earth Observation downstream services industry.
• The technologies to be developed will also extend the linked geospatial data systems GeoTriples,
JedAI, Strabon and SemaGrow pioneered by project partners UoA and NCSR in the past, so that they
scale to the extreme volumes of Copernicus data.

5. 5
The Data Science Pipeline for Linked Earth
Observation Data
Ingestion Processing
Transformation
into RDF
Publishing
Storage, Querying and
Question Answering
Search, Browse,
Explore and Visualize
Cataloguing
Archiving
Dataset Discovery
Knowledge Discovery
Interlinking

6. 6
The Data Science Pipeline (cont’d)
Ingestion Processing
Transformation
into RDF
Publishing
Storage, Querying and
Question Answering
Search, Browse,
Explore and Visualize
Cataloguing
Archiving
Dataset Discovery
Knowledge Discovery
Interlinking

7. 7
The Food Security Use Case
• The objective is to develop high resolution water availability maps for agricultural areas allowing a
new level of detail for wide-scale irrigation support for farmers. The Danube and Duero catchments will
be targeted.

8. 8
Knowledge Discovery with Scalable Deep Learning
Architectures
• Developed an LSTM deep neural network architecture for crop type mapping from Sentinel 2 data.
This has been implemented on Hopsworks and it is being used in the Food Security use case.

9. 9
Very Large Training Datasets for Deep Learning
Architectures
• Developed a training dataset consisting of ~1M pixels of 16 Sentinel 2 images located in Austria
where each pixel is labelled with one of 13 crop types. This dataset was developed using existing crop
type maps and Sentinel 2 data and it was used to train the LSTM network for the Food Security use
case.
• Available publicly very soon (http://earthanalytics.eu/datasets.html).

10. 10
Transformation into RDF
• Developed the system GeoTriples-Spark for transforming big geospatial data from their legacy formats
into RDF. GeoTriples-Spark can transform 2TBs of geospatial data into RDF in 50 minutes.
• See:
o George Mandilaras and Manolis Koubarakis. Scalable Transformation of Big Geospatial Data into
Linked Data. Submitted. 2021.
o https://github.com/LinkedEOData/GeoTriples

11. 11
Interlinking
• Developed the system JedAI-spatial for interlinking big linked geospatial data. JedAI-spatial has been
tested with >100 GB of geospatial data and has been shown to scale almost linearly. For a dataset
with 72M x 115M geometries, it computes all DE9IM topological relations in less than 50 minutes.
• See:
o George Papadakis, Georgios Mandilaras, Nikos Mamoulis and Manolis Koubarakis. Progressive,
Holistic Geospatial Interlinking. The Web Conference 2021, April 19 - 23, Ljubljana, Slovenia, 2021.
o https://github.com/giantInterlinking/prGIAnt

12. 12
Storage, Querying and …
• Developed the system Strabo2 for querying big linked geospatial data using the OGC standard
GeoSPARQL. Strabo2 can process queries of the Geographica benchmark over 171GBs in 30 to
400 seconds.
• Developed the version 3 of system Semagrow for federating big linked geospatial data sources.
Semagrow can process queries used in the Food Security use case over <1GB of data in <20
seconds.

13. 13
… Question Answering
• Developed GeoQA, the first question answering engine for linked geospatial data.
• See:
o Dharmen Punjani, Markos Iliakis, Theodoros Stefou, Kuldeep Singh, Andreas Both, Manolis
Koubarakis, Iosif Angelidis, Konstantina Bereta, Themis Beris, Dimitris Bilidas, Theofilos Ioannidis,
Nikolaos Karalis, Christoph Lange, Despina-Athanasia Pantazi, Christos Papaloukas and Georgios
Stamoulis. Template-Based Question Answering over Linked Geospatial Data.
https://arxiv.org/pdf/2007.07060.pdf
• http://geoqa.di.uoa.gr/

14. 14
Search, Browse, Explore and Visualize
• We always use the visualization tool Sextant (http://sextant.di.uoa.gr/)

15. Thank you!
Visit our Web site: http://earthanalytics.eu/
Follow us on Twitter:
@ExtremeEarth_EU @mkoubarakis