Satellite Events

BiDS’23 kicks off with a dedicated day of Satellite Events on Monday November 6th, such as community contributed Tutorials, Hackathons and Challenges. 

Additionally, formats such as Lightning Talks and Birds of a Feather sessions will run throughout the conference from November 7th to 9th.  


6 September 2023  

Organisers webpages with information and material will be available for attendees

14 September 2023

Registration Opening for Satellite Events

6 November 2023

BiDS’23 Satellite Events day

7 - 9 November

Lightning Talks and Birds of Feather

Satellite Events Programme

Please click HERE to download the programme.

What type of Satellite Events will be present at BiDS’23? 

Tutorials: instructed hands-on tutorials, where users experiment and follow along using their own laptops;

Hackathons: in-depth coding events, ideally organized and initiated to begin prior to BiDS’23, while the final work and awards ceremony can be done at BiDS’23;

Challenges: competitions on thematic/methodological topics where participants develop solutions and compete for the best idea/development, awards ceremony can be done at BiDS’23;


BiDS’23 Satellite Events

1. Ideating the Impact of DestinE: a collaborative design session [Challenge, 4.5 hours]

DestinE is an initiative by European Commission services implemented by ECMWF, ESA and Eumetsat (3E) to develop a highly accurate digital model of the Earth on a global scale ( One of the strategic goals of this initiative involves the development of a community of interested parties and individuals to help co-create this vision. At this moment, the community drives the project in areas of its interest seeks to co-create visualisations of possible futures or mock-ups of services where DestinE will be fully operating.

In this challenge, participants will be immersed in the project’s scope by 3E members, and with the aid of facilitators will be imagining possible, creative, and preferable ways that DestinE may impact end user’s lives. The end of the session will find participants having worked in teams and having created visualisations and low fidelity mock-ups of end DestinE services that could potentially move from fiction to reality in the future.

The final visualisations of fictional services will be up for awards based on their alignment with the DestinE vision, their impact on people’s lives and less on what is currently technically feasible. Therefore, participants are invited to bring along their imagination.

Organisers and expected contributors: Eleni Karachaliou (Aristotle Univeristy of Thessaloniki), Costas Bissas (Aristotle Univeristy of Thessaloniki), Aikaterini Bakousi (Aristotle Univeristy of Thessaloniki), Antonio Romeo (RHEA Group), Rob Carillo (Trust-IT), Alexis Longuet (Serco) 

Please find more information on the Webpage:

2. Using Jupyter Notebooks in the Copernicus Dataspace Ecosystem [Tutorial, half day]

Copernicus Data Space Ecosystem is the new data infrastructure for accessing and analyzing Sentinel satellite imagery. Replacing

Copernicus Open Access Hub during Autumn 2023, the Data Space Ecosystem aims to facilitate cloud processing instead of downloading imagery locally. The Copernicus Dataspace Ecosystem Jupyter Lab is one of the most user-friendly programming environments with integrated codebase and seamless access to the full image archive.

The objective of this tutorial is to enable remote sensing practitioners and developers to quickly develop scaleable algorithms, making the most of API access, Python libraries and the Dataspace. Through hands-on work with Jupyter Notebook examples participants will learn to use the Catalog, Processing, Statistics and Batch APIs, a toolkit which enables efficient analysis of large areas. The examples will be relevant for environmental research, using various Sentinel data sources with a minimal amount of auxiliary data. The tutorial presents workflows that investigate long-term time series of imagery that would not be feasible without streamlined data access. The new Copernicus Data Space Ecosystem allows to directly access the data they need for analysis without having to download large amounts of imagery. Participants will learn sufficient skills to create similar workflows for their own research questions and development tasks.

The audience should be familiar with the basics of Earth Observation and coding in Jupyter Notebooks.

Organisers and Instructors: Jonas Viehweger (Sentinel Hub Gmbh), Megha Devaraju (Sentinel Hub Gmbh)and András Zlinszky (Sinergise d.o.o) 

3. Large Geospatial data handling in Julia [Tutorial, half day]

We need tools to efficiently analyse the increasing stream of available remote sensing data. Spatiotemporal data cubes are becoming ever more abundant for this and are widely used in the Earth Observation community to handle geospatial raster data.

Sophisticated frameworks in high-level programming languages like R and python allow scientists to draft and run their data analysis pipelines and to scale them in HPC or cloud environments.

While many data cube frameworks can handle harmonized analysis-ready data cubes very well, we repeatedly experienced problems when running complex analyses on multi-source data that was not homogenized. The problems arise when different datasets need to be resampled on the fly to a common resolution and have non-aligning chunk boundaries, which leads to very complex and often unresolvable task graphs in frameworks like xarray+dask.

In this workshop we present the emerging ecosystem of large-scale geodata processing in the Julia programming language under the JuliaDataCubes github umbrella.

Julia is an interactive scientific programming language, designed for HPC applications with primitives for Multi-threaded and Distributed computations built into the language.

We will demonstrate an example analysis where data from different sources (Sentinel-1, Sentinel-2, high- resolution land cover), summing to multiple TBs of data, can interoperate on-the-fly and scale well when run on different computing environments.

Organisers and expected contributors: Felix Cremer (Max-Planck Institute for Biogeochemistry), Lazaro Alonso (Max-Planck Institute for Biogeochemistry)

Please find more information in the repository:

4. Open Science with NASA-ESA-JAXA EO Dashboard [Tutorial, half day]

EO Dashboard project has been developed by NASA, ESA (the European Space Agency), and JAXA (Japan Aerospace Exploration Agency). The three agencies and have combined their resources, technical knowledge, and expertise to produce the Earth Observing (EO) Dashboard (, which aims to strengthen our global understanding of the changing environment with human activity. The EO Dashboard presents EO data from a variety of EO satellites, served by various ESA, NASA, and JAXA services and APIs in a simple and interactive manner. Additionally, by means of scientific storytelling, it brings the underlying science closer to the public providing further insights into the data and their scientific applications. In this tutorial participants use the EO Dashboard and its resources to do Open Earth Observation Science. Through very practical exercises hosted on the EOxHub JupyterLab Workspace in the Euro Data Cube environment, the participants will access open data sources of EO Dashboard, will use the open APIs and services to work with this data and will create workflows to extract insights from the data. Finally, they will learn how to create and publish stories, data and code on EO Dashboard following FAIR and Open Science principles. Free access to the Euro Data Cube resources will be ensured via the ESA Network of Resources sponsoring mechanism.

Organisers and expected contributors: Anca Anghelea (ESA),  Manil Maskey (NASA),  Naoko Sugita (JAXA),  Shinichi Sobue (JAXA),  Kaori Kuroiwa (RESTEC/JAXA), Lubomir Dolezal (EOX), Olaf Veerman (Development Seed/NASA),  Federico Rondoni (RHEA GROUP), Sara Aparicio (SOLENIX)

Please find more information in the EODASH Repository:,                      Notebooks:                                            and Website: 

5. EOEPCA Open Source EO Exploitation Platform Hands-on [Tutorial, 1.5 hrs]

Exploitation platforms offer a cloud-based virtual environment where expert users access data, develop algorithms, conduct analysis and share their value-adding outcomes. EOEPCA provides an out-of-the-box solution for an EO Exploitation Platform that integrates open-source components interfacing through open-standards. This tutorial provides a hands-on session using Jupyter notebooks that demonstrate the capabilities of each platform component. The Jupyter notebooks will lead each participant through the workflow of registering, discovering, accessing and visualising data in their own user workspace; preparing and packaging an application for deployment to the platform; and executing the application using data harvested within the platform.

Organisers and Contributors: Richard Conway (Telespazio UK Ltd);  Angelos Tzotsos (EOfarm); Fabian Schindler (EOX); Gérald Fenoy (GeoLabs); Simone Vaccari (Terradue)

Please find more information in the repository:


6. Open & Reproducible workflows in Earth Observation (OREO) [Tutorial, 1.5 hrs]

Many cloud-based solutions for workflows in EO are available to users today, but only few support reproducibility or comply with FAIR data principles. In this tutorial we will demonstrate three solutions that meet these requirements: openEO process graphs, OGC Best Practice for EO Application Package and Deep ESDL workflows. Participants will be able to follow along using Jupyter lab notebooks, get familiarized with basic concepts and exemplify reproducibility for a set of use cases using workflows based on all three approaches. Users will also learn first-hand how these approaches are used in practice to build capacity on EO Open Data Science in the cloud (in the context of ESA’s Cubes and Clouds MOOC) and to enable reproducibility of algorithms feeding the NASA-ESA-JAXA EO Dashboard.

Instructors: Anca Anghelea (ESA), Claudio Iacopino (ESA), Patrick Griffiths (ESA)

Please find useful resources at the following links:                                                                                                        


7. AI4EO - Building Sustainability: Using Artificial Intelligence for Estimating Construction Year from multi-modal street-view – EO dataset [Challenge, 1.5 hrs]

Join the AI4EO Challenge at the Big Data from Space 2023 Conference and contribute to the progress on estimating building construction years using multi-modal deep learning. The challenge will use a unique and comprehensive dataset across Europe, including building footprints, construction year information, street-level imagery, and cloud-free very high-resolution and Sentinel-2 satellite imageries. Participants will utilise the dataset to train their deep-learning models and compete for accurate construction year estimations. The challenge dataset and associated resources will be made available on an online repository, promoting transparency, reproducibility, and collaboration within the research community.

Overall, this challenge will provide an opportunity to leverage the power of big data analytics in space to enhance our understanding of building construction patterns, improve urban planning strategies, advance urban building sustainability and expand the field of deep learning. Don't miss this opportunity to contribute to the advancement of big data analytics in space!

Organisers and Instructors: Nicolas Longepe (ESA), Bertrand Le Saux (ESA), Nikolaos Dionelis (ESA), Enrico Ubaldi (MindEarth), Nika Oman Kadunc (Sinergise), Devis Peressutti (Sinergise), Annekatrien Debien (SpaceTech Partners); Mattia Marconcini (MindEarth); Alessandra Feliciotti (MindEarth)

You will find more information in the repository in due time.

8. Using EO Earth Observation Datacubes for Understanding the Earth’s Rapid Changes [Hackathon, 4.5 hrs + preparation before BiDS]

The Earth is constantly changing and reliably understanding these changes is key to supporting today’s most pressing issues such as environmental degradation, biodiversity loss, and natural disasters. However, reliably understanding and tracking changes as they occur has been a historically difficult task due to the challenges of working with large remote-sensing data sets from different missions. With the emergence of many machine learning-based tools, platforms able to make data normalization easier, and the cloud to manage Big Data volumes and processing, it is now feasible to develop effective tools to understand the rapid changes of the Earth and take action for environmental protection and promoting sustainability.

Participants will work together in a hackathon to develop automated tools to classify and understand land changes in order to better understand our evolving planet. Examples include automated monitoring of rivers and water bodies to quantify the recession and expansion, detecting deforestation in near-real-time, assessing forest fire impact during and after fires, or evolution of green landscape infrastructure like hedges supporting biodiversity. Study sites will be provided to participants to explore, analyze and develop a suite of innovative open tool to automatically understand the changing landscape and support today’s environmental challenges.

Participants should be comfortable using Jupyter notebooks and will have knowledge in one of the following areas: remote sensing, data science, data processing, or data visualization. Participants will work together to develop innovative solutions to automatically detect changes and/or visualize those changes in a way to facilitate Big Data analysis.

Contributors and Instructors: Nicolas Karasiak (EarthDaily Agro), Rudy Schueder (EarthDaily Analytics), Peter McElroy (EarthDaily Analytics), Cécile Tartarin (EarthDaily Agro), Andrew Mullin (EarthDaily Agro), Robin Cole (EarthDaily Analytics), Harold Clenet (EarthDaily Agro), Rick Chern (EarthDaily Analytics), Vincent Lelandais (EarthDaily Agro), William Parkinson (EarthDaily Analytics), Chris Rampersad (EarthDaily Analytics).

To register to the preparatory event and find more information, please access:


9. Working with EUMETSAT and Copernicus marine and atmospheric data across multiple cloud-based systems [Tutorial, half day]

EUMETSAT has developed a prototype of a multi-cloud framework which allows the scalable access and processing of Copernicus and EUMETSAT data for marine and atmospheric composition applications. The framework is deployed across different cloud systems, including the European Weather Cloud and the Copernicus WEkEO DIAS. This hands-on tutorial session will give an opportunity to explore the use and benefits of such a framework and will consist of three main parts. The first part will provide an introduction of common terminologies used in the context of cloud computing and Earth observation applications, including an overview of the current Earth observation cloud landscape and cloud-native geospatial tools. The second part will practically introduce the data-proximate, multi-cloud framework developed by EUMETSAT, ECMWF, and UKMO, with a specific focus on how the Python library for distributed and parallel computing, Dask, can be extended from a single- to a multi-cloud framework using a mesh-network architecture. The third part will then highlight two concrete application examples focused on EUMETSAT marine and atmospheric composition data.

This tutorial has a strong hands-on component during which participants will have ample time to explore the developed framework based on application case studies. It is suitable for a diverse audience, including beginners as well as more advanced users of cloud- based systems. A basic knowledge of Python programming will be required.

Organisers, expected Instructors and Contributors: Ben Loveday (EUMETSAT/Innoflair UG), Julia Wagemann (EUMETSAT/MEEO SRL) Hayley Evers-King (EUMETSAT), Federico Fierli (EUMETSAT), Mike Grant (EUMETSAT), Roope Tervo (EUMETSAT), Joana Miguens (EUMETSAT), Anna-Lena Erdmann (EUMETSAT), Armagan Karatosun (ECMWF)

Resources will be made available at: 

10. Exploring the FuseTS Toolbox: Fusing and Analysing Multi-Source EO Time Series Data [Tutorial, half day]

We introduce a comprehensive training program aimed at enhancing Earth observation data analysis skills. This session includes a hands-on open-source FuseTS toolbox developed as part of the AI4FOOD project (ESA).

This training program aims at a deep dive into the capabilities of the FuseTS toolbox. Through its user-friendly Python implementation, participants will learn to seamlessly fuse optical and SAR data, unlocking the ability to conduct advanced time series analytics.

Our training program combines valuable insights with practical application through hands-on exercises, ensuring a well-rounded learning experience. Real-world case studies will be presented, showcasing the successful implementation of the toolbox.

This training program is specifically tailored for professionals in EO observation who want to enhance their data analysis capabilities. Regardless of your expertise level, this program provides the necessary insights to leverage multi-temporal fusion for real-world applications.

We look forward to exploring the FuseTS toolbox with you and helping you take your data analysis to the next level.

Planned Contributors: Matic Lubej (Sinergise), Darius Couchard (VITO), Jochem Verrelst (University of Valencia), Jeroen Dries (VITO) and Bram Janssen (VITO). 

Please find more information on the website:                                                                and in the repository:

11. Mastering Earth Observation Application Packaging with CWL [Tutorial, 1.5 hrs]

Join us for the "Mastering Earth Observation Application Packaging with CWL" tutorial event, where we will dive into the world of EO Application Packages and explore how to effectively package, share, and execute Earth observation workflows using the Common Workflow Language (CWL) standard.

This tutorial event is designed for developers, scientists, and Earth observation enthusiasts who want to enhance their skills in creating and sharing EO Application Packages. Whether you are new to CWL or already have some experience, this event will provide valuable insights and practical knowledge to boost your expertise.

During the event, you will learn:

  • The fundamentals of EO Application Packages and their role in the Earth observation domain.
  • How to leverage CWL to describe, package, and share workflows.
  • Techniques for incorporating data, code, configuration files, and documentation into an EO Application Package.
  • Best practices for creating portable and reproducible Earth observation workflows.
  • Hands-on exercises to reinforce your understanding and gain practical experience.

This tutorial will guide you through step-by-step tutorials, demonstrating the process of creating EO Application Packages using CWL. Don't miss this opportunity to enhance your skills in Earth observation application packaging and embark on a journey towards mastering EO Application Packages with CWL.

12. Big Earth observation data analysis using satellite image time series [Tutorial, 4.5 hrs]

This tutorial presents a hands-on introduction to Big Earth observation (EO) data analysis using satellite image time series, using the open-source R package sits that provides an end-to-end solution for land classification, with a broad functionality:

  • Retrieval of big EO data for STAC-compliant cloud provides, including; AWS, USGS, Planetary Computer, NASA HSL, Digital Earth Africa, Swiss Data Cube and Brazil Data Cube.
  • Regularization of data cubes from Sentinel-2, Landsat, MODIS imagery (Sentinel-1 data from June 2023).
  • Extraction of time series samples from data cubes;
  • Band operations and mixture models;
  • Training data quality control using an innovative SOM-based approach;
  • Methods for correcting imbalance in training samples;
  • Machine learning methods for time series, including random forest, SVM, xgboost, multilayer perceptrons, temporal CNN, residual networks, and temporal self-attention encoder;
  • Tuning methods for deep learning hyperparameter optimization;
  • Efficient parallel processing for large-scale data ML classification, including GPU support;
  • Validation and accuracy support FAO-recommended best practices for classification evaluation;
  • Spatial uncertainty estimation of classification results;
  • Active learning to reduce bias and errors in sample selection;
  • Ensemble prediction from multiple models.

The package has reached NASA TRL level 9, and is used operationally by Brazil’s National Institute for Space Research to produce large-scale land use and land cover maps for the Amazonia and Cerrado biomes. This tutorial is based on the on-line book available at and on the Kaggle notebooks available at

Contributor: Prof. Dr. Gilberto Camara (INPE). 

The tutorial will be based on the on-line book, "sits: Satellite Image Time Series Analysis on Earth Observation Data Cubes”, available at 

13. The Earth Observation Training Data Lab (EOTDL) – A Hands-On Tutorial [Tutorial, half day]

The Earth Observation Training Data Lab (EOTDL, is, on the one hand, a repository of training datasets and pre-trained ML models for Earth Observation applications. On the other hand, EOTDL is an opensource ecosystem providing tools and processing resources for creating training datasets and train ML models in the cloud. On this hands-on tutorial you will learn how to:

  • Explore and download the available training datasets and pre-trained ML models.
  • Train an ML model with a dataset locally or in the cloud.
  • Ingest an existing dataset or model into the environment.
  • Create your own training dataset from scratch leveraging EOTDL’s tools such as data access, data curation and labelling.
  • Contribute to existing datasets with more annotations or validation.
  • Get familiar with more advanced features such as versioning, feature engineering, etc.
  • Access the different EOTDL layers (libraries, CLI, API, user interface).
  • Get involved with the EOTDL community an contribute with new datasets, ML models and features.

Whether you are a beginner or an experienced practitioner in Artificial Intelligence for Earth Observation applications, the EOTDL is an excellent resource for enhancing your skills and contributing to the community. For this hands-on session you will need to bring your laptop.

Contributor: Juan B. Pedro (CTO at EarthPulse, EOTDL project manager).

Please find further information at: and 

14. AI4Copernicus tools and methods for bridging AI and EO [Tutorial, half day]

Artificial Intelligence (AI) represents a collection of tools and methodologies that have the potential of transforming virtually all aspects of human activity. Earth observation (EO) data, including satellite and in-situ, are essential for a number of applications, covering high-impact domains as diverse as security, agriculture and health. The H2020 AI4Copernicus project ( delivers a technological framework for developers and businesses to combine AI-infused tools and EO data and services in order to create high impact applications. This is further facilitated by providing a bridge between DIAS platforms and the European AI-on-Demand platform.

This tutorial will present the main technological assets AI4Copernicus brings to the table as well as its methodology and tools for linking the DIAS and AI-on-Demand platforms, through appropriately selected use-cases during a hands-on session. Indicatively, technological assets AI4Copernicus contributes to the community include the following: Sentinel-1 and Sentinel-2 pre-processing chains, Deep network for pixel-level classification of S2 patches, Probabilistic downscaling of CAMS air quality model data, and many others. In addition, AI4Copernicus contributed semantics-based tools for visualisation and discovery of complex EO data. More information can be found in the AI4Copernicus Technical Documentation.

The choice of the use cases for the hands-on applications will come from AI4Copernicus' rich collaborations with 3rd parties who specialise in EO applications in the fields of Security, Health, Agriculture, Climate and others, and will be consolidated nearer the time of the tutorial.

This tutorial will target EO and AI specialists and will require little IT technical background. The hands-on session will take place on the CREODIAS infrastructure and will involve the application of AI techniques on available datasets.

Organisers, Instructors and Contributors: Antonis Troumpoukis (National Centre for Scientific Research Demokritos), Despina-Athanasia Pantazi (National and Kapodistrian University of Athens), Omar Barrilero (European Union Satellite Centre), Giulio Weikmann (University of Trento), Mohanad Albughdadi (ECMWF), Vasileios Baousis (ECMWF), Iraklis Klampanos (National Centre for Scientific Research Demokritos), Jacek Tokarski (CloudFerro), Lorenzo Bruzzone (University of Trento), Michele Lazzarini (European Union Satellite Centre), George Stamoulis (National and Kapodistrian University of Athens), Manolis Koubarakis (National and Kapodistrian University of Athens).

Please find further information at: 

15. Introduction to Pangeo [Tutorial, 1.5 hrs] 

The Pangeo community offers a training session that will guide you through the Pangeo ( ecosystem to learn about open, reproducible, and scalable Earth science. The workshop is designed to help anyone interested in starting their journey with Pangeo while avoiding common pitfalls. Participants will have the opportunity to learn about the following: 

  • Accessing data, 
  • Load and analyze data with Xarray 
  • Visualize data with Hvplot 
  • Understand how to scale with Dask 

All the Python packages used during this training are Open-source. 

The Training material is open-source (CC-BY-4) too. 

The workshop aims at empowering attendees to learn new skills and build confidence in using them in their work. This workshop will assume prior knowledge of the Python programming language and basics of Xarray. We recommend learners with no prior knowledge of Python or Xarray to get familiar with them, for instance using Software Carpentry training material (, Project Pythia (, the xarray tutorial (, or Pangeo Galaxy Training material ( 

The tutorial will have work along with hands-on exercises to check the understanding of attendees. 

Organisers, Instructors and Contributors: Tina Odaka (IFREMER), Anne Fouilloux (Simula Research Laboratory), Justus Magin (IFREMER) and Pier Lorenzo Marasco (SEIDOR)

Please find further information at:                            and


16. Introduction to OpenEO [Tutorial, 1.5 hrs] 

Earth Observation satellites create a growing data archive enabling environmental monitoring services which advance the knowledge about planet earth significantly. openEO Platform builds upon this data archive and allows users to access and process Earth Observation data for their needs on a federated infrastructure. This approach exhibits several advantages:  

  • Firstly, the user does not need to download, store, and handle large amounts of Earth Observation data. 
  • Secondly, the federated compute platform enables the user to process data fast and facilitates computations at large scale. 
  • Lastly, users can easily share their analysis with other uses which simplifies the reproducibility of scientific projects.  

openEO Platform builds on the successful development of the openEO Application Programming Interface (API) which was developed in the Horizon 2020 project openEO (2017–2020, see The openEO project defined a common set of analytic operators for Earth Observation analysis which was implemented by several backends. This common architecture was expanded by an aggregation layer to openEO Platform, an operational, federated service running at EODC, VITO and Sinergise.  

In this tutorial session we will showcase the use of the platform via Python Jupyter Notebooks and a graphical user interface. The session will cover: Sign up, Sign in, submitting first small jobs (fast synchronous requests vs batch jobs) and a short introduction to larger scale processing and UDF's for running custom code. 


17. Scaling Big Data Analysis with Pangeo and OpenEO: Unlocking the Power of Space Data [Tutorial, half day] 

Pangeo and openEO offer a training session that will highlight the complementarities of Pangeo ( and openEO ( ecosystems for developing efficient Big Earth science data pipelines. The workshop will start with a short introduction of Pangeo and OpenEO with a goal to teach attendees how to fully exploit both frameworks to run complex data workflows. 

All the Python packages used during this training are Open-source. Sample datasets used in the tutorial are EO datasets that are freely available to everyone and can also be used for real scientific analysis. 

The Training material is open-source (CC-BY-4) too. 

The workshop aims at empowering attendees to learn new skills and build confidence in using them in their work. The tutorial will have work along with hands-on exercises to check the understanding of attendees. Multiple opportunities to ask questions and discuss with the Pangeo and openEO communities will be offered. 

Organisers, Instructors and Contributors: Tina Odaka (IFREMER), Anne Fouilloux (Simula Research Laboratory), Justus Magin (IFREMER), Ola Formo Kihle (University of Trømso, Norway), Pier Lorenzo Marasco  (SEIDOR), Benjamin Schumacher (EODC), Lukas Weidenholzer (EODC), Valentina Hutter (EODC), Christoph Reimer (EODC), Christian Briese, EODC, Daniel Thiex (Sinergise), Pratichhya Sharma (VITO),  Jeroen Dries (VITO), Michele Claus (EURAC), Alexander Jacob (EURAC), Patrick Griffiths (ESA)

Please find further information at:                            and

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Questions regarding Satellite Events organisation may be directed to