Our mission : In a world that is increasingly mobile, interconnected and interdependent, the security of people and goods, infrastructure and nations depends on leaders and organizations and their ability to decide and act in a timely fashion and obtain the best outcomes. In the markets that Thales serves - defense, security, space, aerospace and ground transportation - these decisions are often of critical importance. Navy, army and air force commanders, as well as air traffic controllers, policymakers and infrastructure operators, face these critical decisions and need full, relevant and reliable information to understand the situation and make the right choices.

Atos is a global leader in digital transformation with approximately 100,000 employees in 72 countries and annual revenue of around € 12 billion. The European number one in Big Data, Cybersecurity, High Performance Computing and Digital Workplace, The Group provides Cloud services, Infrastructure & Data Management, Business & Platform solutions, as well as transactional services through Worldline, the European leader in the payment industry. With its cutting-edge technologies, digital expertise and industry knowledge, Atos supports the digital transformation of its clients across various business sectors: Defense, Financial Services, Health, Manufacturing, Media, Energy & Utilities, Public sector, Retail, Telecommunications and Transportation. The Group is the Worldwide Information Technology Partner for the Olympic & Paralympic Games and operates under the brands Atos, Atos Consulting, Atos Worldgrid, Bull, Canopy, Unify and Worldline. Atos SE (Societas Europaea) is listed on the CAC40 Paris stock index.
Atos est un leader international de la transformation digitale avec environ 100 000 collaborateurs dans 72 pays et un chiffre d’affaires annuel de l’ordre 12 milliards d’euros. Numéro un européen du Big Data, de la Cybersécurité, des supercalculateurs et de l’environnement de travail connecté, le Groupe fournit des services Cloud, solutions d’infrastructure et gestion de données, applications et plateformes métiers, ainsi que des services transactionnels par l’intermédiaire de Worldline, le leader européen des services de paiement. Grâce à ses technologies de pointe et son expertise digitale & sectorielle, Atos accompagne la transformation digitale de ses clients dans les secteurs Défense, Finance, Santé, Industrie, Médias, Énergie & Utilities, Secteur Public, Distribution, Télécoms, et Transports. Partenaire informatique mondial des Jeux Olympiques et Paralympiques, le Groupe exerce ses activités sous les marques Atos, Atos Consulting, Atos Worldgrid, Bull, Canopy, Unify et Worldline. Atos SE (Societas Europea) est une entreprise cotée sur Euronext Paris et fait partie de l’indice CAC 40.


NEW CAPGEMINI BOILERPLATE: A global leader in consulting, technology services and digital transformation, Capgemini is at the forefront of innovation to address the entire breadth of clients’ opportunities in the evolving world of cloud, digital and platforms. Building on its strong 50-year heritage and deep industry-specific expertise, Capgemini enables organizations to realize their business ambitions through an array of services from strategy to operations. Capgemini is driven by the conviction that the business value of technology comes from and through people. It is a multicultural company of 200,000 team members in over 40 countries. The Group reported 2016 global revenues of EUR 12.5 billion.

Visit us at www.capgemini.com. People matter, results count.

Quantum is a leading expert in scale-out tiered storage, archive and data protection, providing solutions for capturing, sharing, managing and preserving digital assets over the entire data lifecycle. From small businesses to major enterprises, more than 100,000 customers have trusted Quantum to address their most demanding data workflow challenges. Quantum's end-to-end, tiered storage foundation enables customers to maximize the value of their data by making it accessible whenever and wherever needed, retaining it indefinitely and reducing total cost and complexity. See how at www.quantum.com/customerstories

The Intelligence Programme Line of Airbus Defence and Space is the supplier of choice for commercial satellite imagery, C2ISR systems and related services. Airbus Defence and Space has unrivalled expertise in satellite imagery acquisition, data processing, fusion, dissemination and intelligence extraction allied to significant command and control capabilities.
The company is able to create a comprehensive situational awareness picture and deliver sophisticated end-to-end solutions across all commercial, institutional and defence markets. Based upon exclusive commercial access to Pléiades, SPOT, DMC Constellation, TerraSAR-X and TanDEM-X satellites, combined with broad applications experience, the company delivers an extensive portfolio spanning the entire geo-information value chain.

The IRT Saint Exupéry is a technology accelerator for aeronautics, space and embedded systems. Combining resources from public and private partners, it leads integrating collaborative R&T activities within three strategic domains: high-performance multifunctional materials, more electrical aircraft and embedded systems. Associated with technology platforms and high-level skills in Toulouse and Bordeaux (France), it boosts the maturation and transfer of breakthrough technologies (TRL 4-6) to its industrial partners.
The 8 IRTs benefits from the French government Programme “Investissements d’Avenir” (Investments for the Future Programme) to boost high value competitive technological sectors.
www.irt-saintexupery.com – Twitter @irtSaintEx

For more than 35 years, CS SI has been successfully delivering turnkey systems and providing engineering services for space and its applications markets. With a European space workforce of 350 engineers gathering advanced and unique skills in both information technologies and space data engineering, CS SI is a major and proven long-term trusted partner of space and defense agencies, satellite prime contractors and operators, and space application actors.
To face the challenges of earth observation data that are steadily increasing volume, delivery rate, degree of variety, complexity and interconnection of data, CS SI is presenting new processing concepts which ensure the necessary power but also scalability and elasticity to actually exploit those data. CS SI solutions are based on a multi-cloud strategy that allows to always have the right offer, to benefit from a maximum of flexibility and to ensure independency against cloud vendors and big data frameworks.


Aerospace Valley
An example of demo could be FabSpace, where we built an earth observation playground dedicated to students and early users. https://www.fabspace.eu/

Deimos Imaging

Deimos Imaging will demonstrate the initial operational capabilities of its Kanvas platform with outreaching functions like WebApps, story maps and simple imagery discovery services, plus its development and integration capabilities.

Our team would also provide more information on current satellite constellations including Deimos and Pangeo Alliance assets and planned ones (UrtheDaily, SAR-XL and OptiSAR).


EOS Data Analytics Inc.
EOS DA uses a combination of satellite imagery, geospatial data, customer workflow information, and consumer behavior principles to make the deepest and the most comprehensive GIS analysis. People and companies from different industries can fully meet their GIS requirements by using EOS Data Analytics’ service. 
Our solutions, powered by proprietary algorithms, provide users with information in a decision-making form. Our analytics help companies to have the full vision of the current situation and make fast and accurate decisions.

This demonstration will show our land viewer product as well as agriculture crop classification and use case examples if inquired. 

ESA Research and Service Support 
The ESA Research and Service Support (RSS) offers services to ease Earth Observation (EO) data exploitation. RSS users are Principal Investigators, Institutions and SMEs  needing support to progress in their research and/or development activity, In order to support the community interested in the exploitation of EO data, the ESA RSS service provides tools and ad-hoc support that can be easily tailored on the user needs. The RSS service demo shows concrete examples of use of such tools and clarifies the types of ad-hoc support that can be requested by EO data users.

IRT Saint Exupéry
  • Planning optimization of satellite mission with ATLAS: Today’s Earth observation systems are facing major evolutions: more and more satellites, customers, requests. Thus, there is a need to develop new planning systems which can provide mission plans for satellite constellations in a bounded time (<5 minutes) and providing more reactivity to the whole system. To answer those requirements we rely on adaptive multi-agents systems introducing dynamic planning and develop the ATLAS (adaptive satellite planning for dynamic earth observation) planning system. ATLAS has been tested on real spot 6/7 & Pleiades scenarios. Moreover, we have built scenarios with varying number of satellites per constellation to demonstrate the ATLAS scalability.
  • Long-term scheduling with Large Coverage Management: There is a growing operational demand for the acquisition of large area zones (up to several millions of km²) through high resolution observation satellites. Such large coverage projects require a huge number of elementary acquisitions from satellites whenever they fly over the zone, and take up to several weeks or months to reach completion. The objective for large coverage management is to speed up the completion of large coverage projects through the long-term optimization of the acquisition strategy and the combination of multiple Earth Observation (EO) systems. Large coverage management is relevant for system exploitation like Astoterra and Pléiades. Control policies are integrated into existing systems while future EO systems benefit from decisional autonomy through reinforcement learning.
  • Automatic classification: cloud detector and land cover classification: We have investigated solutions to the imagery classification problem in a massive data context. In this case massive means more than 200.000 images. Our work heavily relies on the most recent machine learning technologies giving computers the ability to learn from series of examples. The quantity of data implies the use of scalable and specific technologies such as cloud computing during the design of such framework. Learning as well as testing are based on real spot 6/7 image data sets. The processing chains have been evaluated on tera-scaled Spot 6 database. Two disruptive applications are developed: an automatic cloud detector and an automatic land cover classifier.
  • Automatic object detection: maritime oil slick detection with radar imagery: Within the class of object detection problems, we have built a specific use case whose objective is to enhance existing oil slick detection using a massive radar imagery dataset. We evaluate a machine learning approach to answer this problem. It is implemented in a cloud distributed framework to manage the quantity of data. Learning as well as testing are based on real ENVISAT ASAR and Sentinel 1 image data sets. In order to increase the learning data set with an affordable contribution of human operators, we make use of simulations and characterize the achievable performances. Uses cases include the automatically pointing geographical areas where there are cumulative oil slick detections (natural seep sources).
  • Significant change detection: Sequential observation data are now made available thanks to existing systems like Spot 6/7, Pleïades, Cosmo-skymed, Terrasar and Sentinel. This tendency will be reinforced in a near future with the development of constellations or geostationary observation systems. In this dynamic context, the change detection class of algorithms appears to be well suited to ease the end user task: focus on significant changes. We developed new concepts of change detection chains based on dictionary learning and deep learning state of the art methods, with leading edge robustness characteristics. It has been tested on real Spot 6/7 & Pleiades temporal series. It can be applied to big cities expansion, defense, oil and gas or harbor surveillance.

Magellium implements deep learning methodologies for a large variety of signal and image processing problems from 2015. He has performed internal studies in cloud detection, building detection and developed prototypes for character recognition (OCR) on old manual bathymetry maps (SHOM) or invert multi-spectral images to extract bio-physical parameters. He has developed an operational methodology based on deep learning for ships detection in EO images. Magellium was also in charge of design of a cloud infrastructure adapted to this deep learning-based processing and the deployment of the service. Currently, he performs a study for the CNES to qualify different deep learning free implementations to different use cases of a remote sensing images (planes detection or Martians craters). In the framework of a H2020 project, Magellium will implement a deep learning method to detect agricultural parcels early in the season and distinguish them from other types of vegetation.

eodataservice.org: Earth Observation big data platform to enable multi-disciplinary intelligent planet applications 
In 1999, US Vice-President Al Gore outlined the concept of ‘Digital Earth’ as a multi-resolution, three-dimensional representation of the planet to find, visualise and make sense of vast amounts of geo-referenced information on physical and social environments, allowing to navigate through space and time, accessing historical and forecast data to support scientists, policy-makers, and any other user. 
The eodataservice platform (http://eodataservice.org/) implements the Digital Earth concept in the Big Data era, making use of mostly Earth Observation data: eodatasevice is a cross-domain platform that makes available a large set of multi-year global environmental collections allowing data discovery, visualization, combination, processing and download. It implements a "virtual datacube" approach where data stored on distributed data centers are made available via standardized OGC-compliant interfaces. Dedicated web-based Graphic User Interfaces as well as web-based notebooks, deskop GIS tools and command line interfaces can be used to access and manipulate the data. The platform can be fully customized on users' needs.


Coal And Open-Pit Mining Impacts On American Lands (Coal): A Python Library For Processing Hyperspectral Imagery: This demo will cover the COAL (https://capstone-coal.github.io) Python library providing users with practical insight into how COAL can be used for processing hyperspectral imagery from remote sensing devices such as the Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS). COAL was originally developed as a 2016 – 2017 senior capstone collaboration between scientists at the Jet Propulsion Laboratory (JPL) and computer science students at Oregon State University (OSU) with current development being done by a small but growing community. COAL provides a suite of algorithms for classifying land cover, identifying mines and other geographic features, and correlating them with environmental data sets. COAL is Free and Open Source Software with the pycoal toolkit licensed under the terms of the GPL v2.0.

Satellite Applications Catapult

The Sentinel Data Access Service (SEDAS) was created and funded by the Satellite Applications Catapult and UK Space Agency to help organisations make use of the vast quantities of satellite data becoming available from public and private satellite operators. SEDAS provides a free to use, online data hub offering open access to Earth observation (EO) data from the Copernicus Sentinel 1 and 2 satellites, along with additional datasets from other EO satellites, such as Landsat. Through its web-based interface, users can discover, analyse and download EO data, access related news and educational content, and participate in discussion forums.

SEDAS was designed to:

• Promote and increase the use of space-based applications

• Further scientific understanding through use of satellite data

• Foster growth in the downstream sector through commercial exploitation.

SEDAS users can utilise two different APIs, to discover and download any archived datasets and process them either within CEMS, our purpose built Cloud computing environment for the Space community, or remotely on their own infrastructure, allowing them to build applications that can access the data directly. The internal facing API is an open source product.

Science [&] Technology AS
Onboard intelligent payload processing:
We have, through an ESA GSTP contract, developed an onboard payload data processor for a miniaturized hyperspectral camera. The entire L0-L2a processing chain has been implemented and additional analytics products are developed (land classification map and change detection).
Using big data analytics (machine learning) to generate added value products using Sentinel-2 data:
We have, through an ESA EOEP-4 Innovation contract, developed a forest monitoring service (Silvisense) where we automate the monitoring using machine learning technologies applied to Sentinel-2 imagery. Several technologies have been explored, but the best performing so far is CNN (Convolutional Neural Networks).
Dagger - our scalable novel platform for data processing:
We have developed a containerized, scalable, graph-based processing framework, supporting complex, interdependent and chained processors. The technology development is inspired by our experiences gained through development of the ESA Polar-TEP backend. Dagger can run processors both locally and in remote locations and on cloud platforms.

Space and Intelligence Systems / Harris Corporation

Machine learning regression algorithms are powerful candidates for developing robust and accurate retrieval methods because of their ability to perform adaptive, nonlinear data fitting. Moreover, their real value manifests itself when they are combined with a flexible geospatial framework that is deployed in the enterprise.

The presented Softmax Regression classifier is a machine learning algorithm for spectral-based land cover mapping. It can be created and trained on a reference dataset using spectral and spatial information and then applied to similar data multiple times. Implemented in a classification framework, it provides a flexible approach to customize a classification process. Therefore, it is a substantial contribution to spatio-temporal and time-series analytics of Big Data from Space.

Rasdaman GmbH
We would love to present the rasdaman datacube engine "in action"; rasdaman is
OGC and INSPIRE datacube Reference Implementation, and ESA has assessed rasdaman
in 2017 as "the world leading environment". The European Commission has stated
already in 2015 that rasdaman "with no doubt will change how scientific data get

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