Gisaia IN

DOMINO-X PROJECT CONSORTIUM

  22 Octobre 2021, Media Release

Media release, 22 Octobre 2021

Our team joins others from the French space sectors to form a consortium that has been chosen by the Directorate General of Enterprises (DGE) and Centre National d’Études Spatiales for the DOMINO-X project.

We are proud and happy to contribute to this innovative initiative that will exploit Cloud and Artificial Intelligence technologies to standardize the architectures of ground-based Earth Observation segments and promote the emergence of a modular product and service offering. This will facilitate a need to offer customers lower costs for their requests.

Airbus Defence and Space are leading the DOMINO-X  consortium that brings together other experts from: Airbus, Thales Alenia Space, Safran, CS Group, Orange and Capgemini, Gisaïa, Stack Labs, Human design Group, Geotrend and Leanspace.

Gisaïa will be fronting our solution, ARLAS  for geospatial data analytics . This is not the first time that Gisaïa joins some of the consortium members and offers solutions that simplify the access to Earth Observation products. ARLAS is part of the framework that SOBLOO, Theia and Dinamis are built on. 

ARLAS is open source and built on big data frameworks making it both easily embeddable to other solutions while scaling with need. This is important in the DOMINO-X mission which seeks to explore volumes of Earth Observation data, from diverse product families. But that is not all, ARLAS will also be offering interactive visualisation of the data with a simplified filtering process that makes it easy to quickly sort through data and find what one is looking for. 

The DOMINO-X ultimate quest is to deliver valuable insights for decision making from Earth Observation data by making it accessible to as many users as possible.

For more details contact 

dolphine.rambaud@gisaia.com

 About Gisaïa:

Gisaïa brings seven years of geo-big data analytics expertise, and a robust geo-analytics solution, “ARLAS”. The ARLASframework has been tried and tested in diverse use cases for geospatial intelligence, especially in Earth observation data optimisation.