- Sinergise, Laboratory for geographical information systems, Ltd. (coordinator), Slovenia
- TomTom Global Content BV, Netherlands
- European Union Satellite Centre, Spain
- Technical University Munchen, Germany
- meteoblue AG, Switzerland
Sinergise, a renowned European Earth Observation (EO) company, that visibly contributed towards wider uptake of Copernicus data, is entering the project with its flagship products: EO exploitation platform/services Sentinel Hub and associated Machine Learning library eo-learn. Sentinel Hub has already proven itself as the world’s No. 1 service for the exploitation of Copernicus data. There are currently 75.000 registered users of the service from 159 countries, having access to more than 6 PB of data (300 TB added each month). Sentinel Hub is processing seven million requests per day. Sinergise is providing the project with the ICT technological foundation as well as wide community outreach. The history of operation demonstrates that with Sentinel Hub people across the planet get an instant and seamless insight into Copernicus’ observations, triggering social media campaigns and incentivizing world-renowned newspapers and media along the way. Sentinel Hub already put EO data in the hands of millions of citizens and thereby completely changed the way how people perceive it.
TomTom, one of the world's largest mapping companies, is contributing a rich ground-truth repository, as well as their knowledge of interpretation, both manual and automatic, of features from satellite imagery and GNSS services. They were one of the early adopters of eo-learn, and have since developed their own Earth Cover Engine prototype framework on top, allowing them to evaluate concepts of automated global land cover production with the goal to integrate it in its map, which in turn is integrated to its various navigation services and products. They are bringing a commercialisation angle to the project, ensuring that results will bring revenues and cut production costs later on, assuring the sustainability of the project long after the end of funding.
European Union Satellite Centre - SatCen joined the project with the goal to validate the added value of GEM’s global services through their implementation into a specific Security use-case: Conflict pre-warning service. SatCen’s inputs into the project consist of specific “security” data layers and domain-specific security expertise/knowledge. It is important to note that SatCen is an Agency of the Council of the European Union (EU), a key institution linking Space and Security, and a primary user of EO. Amongst its activities, SatCen is the EC Entrusted Entity to provide the Copernicus Security Service in Support to EU External Action (SEA) and it is a Participating Organization of the Group on EOs (GEO). The Involvement of SatCen will not only bring an unparalleled insight into the inner workings of the security domain but also validate that results of the project can be exploitable for security purposes on the largest possible scale. The complexity of their use-case goes well beyond typical classification ML exercises - it combines various models and data sources as well as decision-making processes, demonstrating the usability of GEM for the widest possible needs and challenges.
Technical University Munchen’s Chair of Remote Sensing Technology - TUM is a joint venture with the German Aerospace Center (DLR) Remote Sensing Technology Institute (IMF). While DLR-IMF is actively involved in the design and operation of ongoing and future EO missions, TUM is dedicated to carrying out method-driven research to process EO data of various modality, scale, and quality. In recent years, the chair put a strong focus on research on topics related to machine learning and artificial intelligence. Since then, TUM developed special competences in processing and analysis of multi-temporal sequences of EO data, as shown by several high-impact publications. Current research interests particularly comprise the multi-temporal modelling of Earth surface processes and efficient strategies for machine learning on EO data, especially the continuous and ongoing update and refinement of learned models or the detection of anomalies in remotely sensed data - thus, TUM being recognised as an ideal project partner to take over the domain of EO focused Machine Learning in GEM.
meteoblue is a Swiss specialist company producing high-precision weather data for the entire world, using observation data, high-resolution Numerical Weather Predictions (NWP), and specialised data output methods adapted to the needs of different user groups. meteoblue offers a wide range of free weather information and specialised tools including climate data, weather history, current weather, and weather forecast for every location worldwide. The company has also a proven track record in (1) the development of special downscaling methods for refinement or adaptation of weather data for diverse customer needs and in (2) the provision of tailored services (for media and websites, outdoor and sports, building management, solar energy, wind energy, agriculture, automotive, transport and logistics, air traffic, science, etc), which makes meteoblue a good fit
for the introduction of climate and weather data into global EO modelling.