EIFFEL will offer the EO-based community the ground-breaking capacity of exploiting existing GEOSS and external datasets, with minimal new data collection activities. Added-value services interoperable with GEOSS will be designed, using cognitive search and metadata augmentation tools based on Artificial Intelligence (AI), including Natural Language Processing. These tools will leverage advanced cognitive features to extract meaningful information from and enrich GEOSS metadata. Moreover, novel methods (super resolution, data fusion) for augmenting the spatiotemporal resolution of explored EO data will be proposed, in order to address the needs of the diverse EIFFEL CC adaptation and mitigation applications. The latter will cover: (i) a set of five different GEO Societal Benefit Areas (SBAs), namely in Water and Land Use Management, Sustainable Agriculture, Transport Management, Sustainable Urban Development and Disaster Resilience domains; (ii) various EU geographical and climatic regions, at local, regional, national, cross-border and pan-European scales. Further, the value of using explainable AI techniques for improving the credibility and comprehensiveness of such CC applications, so that they can offer actionable insights to the decision makers, will be showcased. EIFFEL will foster the co-design of CC adaptation policies and mitigation strategies and monitor CC effects in the respective regions. The project, in line with EuroGEO’s emphasis on early engagement with stakeholders and their participation in the application design, has ensured that they are active consortium members. EIFFEL complies with the framework of results-Oriented GEOSS, to improve the delivery of applications tailored to decision making centres and will actively participate in the GEO Work Programme post-2019. Last, it provides tangible proof of the value of GEOSS data for creating CC applications and encourages projects and initiatives to offer their data through the portal.

The turnover of mining and quarrying in Europe reaches up to 224 billion Euros and has generated EUR 64.9 billion of value added, 1% of the non-financial business economy total. The rise of key enabling technologies and the urbanisation and industrialisation of emerging economies in combination with increase in population and living standards will continue to drive growing demand for raw materials. The need to extract raw materials in a profitable, environmentally sound, and safe way for both mining workforce and communities is driving the mining industry towards innovative approaches to transform operations. Even though Industry 4.0 offers a wide spectrum of solutions, and intelligent technologies to address respective challenges, the mining industry hesitates to adopt such innovative approaches when compared to downstream industries. In addition, the need for a human-centred, environmentally oriented and society-driven approach is emerging in developing Industrial Internet of Things technologies for mining. Dig_IT will address the needs of the mining industry to move forward towards a sustainable use of resources while keeping people and environment at the forefront of their priorities. In order to achieve that Dig_IT proposes the development of a smart Industrial Internet of Things platform (IIoTp) that will improve the efficiency and sustainability of mining operations by connecting cyber and physical systems. The platform will collect data from sensors at 3 levels: (i) human, (ii) assets, (iii) environment and will also incorporate both market real time and historical data. The impact of Dig_IT to the European Mining industry, but also the society itself, can be summarised in the following (with a horizon of 4 years after project ends): (i) increase of the mining efficiency by 17%, (ii) increased OEE for machines and loading by 20% and 18% respectively, (iii) 19% reduction of CO2eq, (iv) about 310 new jobs created and (v) over 28M EUR ROI for the consortium.

The main objective of ERROR is to develop a new software tool, which will offer to the clinician the possibility to assess alternative imaging and therapeutic protocols, in real time, in silico, in order to minimize patient dose, while maintaining image quality of therapeutic effect. This tool will be designed, implemented and evaluated with specific focus on pediatric patients, since this is a rather sensitive target group, where dose considerations are high and no standard protocols and solutions exist. The project will exploit the new generation of computational anthropomorphic phantoms, in combination with well validated Monte Carlo simulations and Machine Learning Tools. In this way, it is envisaged that advanced, yet mature technologies will be integrated, to provide a novel tool, which can lead to a final product. The ERROR project brings together a multidisciplinary consortium of specialists in different areas of medical physics, biomedical engineering, physicians and computer engineers, who will join forces in order to design, implement and clinically assess a novel software tools, which initial focus in the optimization of diagnostic and therapeutic protocols for pediatric exams. Two new SMEs will provide their expertise, as well as investigate the ways to exploit project outcome. A well planned exchange program among academic and industrial partners will facilitate knowledge sharing, maximize collaborative work and finally achievement of project objectives. The consortium, being aware of the scientific and social importance of pediatric clinical applications, has planned a series of dissemination and training activities, aiming at making project knowledge and outcomes available to the scientific community and society.