DEIMOS ENGENHARIA SA

The main objective of BETTER is to implement an EO Big Data intermediate service layer devoted to harnessing the potential of the Copernicus and Sentinel European EO data directly from the needs of the users. BETTER aims to go beyond the implementation of generic Big Data tools and incorporate those tools with user experience, expertise and resources to deliver an integrated Big Data intermediate service layer. This layer will deliver customized solutions denominated Data Pipelines for large volume EO and non-EO datasets access, retrieval, processing, analysis and visualisation. The BETTER solutions will focus in addressing the full data lifecycle needs associated with EO Big Data to bring more downstream users to the EO market and maximise exploitation of the current and future Copernicus data and information services. BETTER developments will be driven by a large number of Big Data Challenges to be set forward by the users deeply involved in addressing the Key Societal Challenges. The World Food Programme, the European Union Satellite Centre and the Swiss Federal Institute of Technology - Zurich working in the areas of Food Security, Secure Societies and GeoHazards will be the challenge promoters. During the project each promoter will introduce 9 challenges, 3 in each project year, with an additional nine brought by the “Extending the market” task, in a total of 36 challenges. The Data Pipelines will be deployed on top of a mature EO data and service support ecosystem which has been under consolidation from previous R&D activities. The ecosystem and its further development in the scope of BETTER rely on the experience and versatility of the consortium team responsible for service/tool development from DEIMOS and Terradue. This is complemented by Fraunhofer Institute’s experience in Big Data systems, which brings to the consortium transversal knowledge extraction technologies and tools that will help bridge the current gap between the EO and ICT sectors.

In GAMMS we will develop an autonomous terrestrial mobile mapping system; i.e. a mobile mapping system (MMS) robot for geodata acquisition and an AI-based highly automated mapping software. In contrast to today’s manned MMS whose cost is dominated by 2- to 3-people crews, we envision fleets of low-cost, autonomous, electrically-powered land vehicles, carrying mobile mapping systems (MMS) and collecting geodata in a massive, continuous way. Although we will develop generalpurpose geodata acquisition and processing techniques, in GAMMS we focus on the rapidly growing market of the High Definition (HD) maps for the autonomous vehicles (AVs), a.k.a. self-driving cars. Because of the enormous task of mapping the world roads for AVs we will develop highly automated software to produce HD maps from the MMS remote sensing data. Because of the safety requirements of AVs, we will also develop map certification methods and quasi real-time, online techniques to continuously update the HD maps. The building blocks of GAMMS are: an electrically-powered AV, a MMS, a GNSS/Galileo receiver, multi-sensor trajectory determination software, multispectral laser scanners, vehicle dynamic models, automated mapping software and mission risk analysis methods. A keystone of GAMMS –which encompasses the extension of the Galileo receiver and the development of ultra-safe, ubiquitous navigation methods at the 5 cm error level– is the use of Galileo features (e.g. E5 AltBOC signal) and new services: navigation message authentication (NMA), high-accuracy serive (HAS) and signal authentication. Galileo and our trajectory determination methods enable the GAMMS concept. Our market value proposition is the production of high-accuracy high-reliable maps at a fraction of today’s cost. In a first fielding of the AMMS technology we will focus on the skyrocketing market of HD maps and, for this particular application, our value proposition includes the quasi real-time, continuous online