According to current discussion within the community of stakeholders, there is in the Geographic Information (GI) sector a gap between the knowledge currently being offered by the European universities and the knowledge and skills requested by the enterprises and public authorities. This is partly due to a fast technological development but also due to societal changes, for instance the EU INSPIRE directive and the e-government action plans within the member states.giCASES, as a Knowledge Alliance project, aimed to enable and strengthen innovation in GI education and industry and to facilitate the collaborative creation, management and sharing of knowledge.These objectives have been addressed by developing new methods for co-creation of knowledge, where industrial partners and universities jointly developed new case-based learning materials by facilitating the exchange flow and co-creation of this knowledge. The project specifically addressed the Geographic Information (GI) sector.The giCASES method for co-creation of knowledge consists of process patterns, supplementary material and a collaboration platform. A process pattern is here a general description of a business process, which may be slightly modified in a particular case in order to suit the specific conditions better. All case studies are different from each other, but the processes they use to co-create knowledge have many characteristics in common which are captured in giCASES by the concept of business process patterns.giCASES considered the following paradigms:• co-creation of knowledge: the process through which two or more organizations and/or actors interact with each other in a collaborative fashion to generate learning content. • case studies: consisting of a real-world problem, which tackle specific topics and issues and has well-defined scopes, learning outcomes, results, actors and corresponding roles, and is addressed in a learning environment. • case-based (collaborative) learning: the educational approach adopted within the giCASES project, where knowledge is cooperatively produced by all the actors involved in the specific case studies planned. • (case-based) learning/training material: the whole of materials produced and/or used to co-create knowledge. • (case-based) learning/training tools: the solutions and technical tools adopted to store, visualize, reproduce, present or aid the development and exchange of learning/training material as well as the results of the learning process.The specific objectives of the giCASES project have been to improve the quality and relevance of GI courses provided by the University members of the consortium, to facilitate the growth of new knowledge-sharing processes and tools between enterprises and universities.In the approach taken in this project, enterprises and academia collaborated both when creating learning material based on real cases and during the courses. Collaborative web-tools have been identified to support the collaboration and co-creation of knowledge among different stakeholder groups. The new learning material and collaborative teaching have been tested in university settings. The project outcomes are provided under open licenses. In order to optimize the spread and usage of the project results, dissemination actions have been specifically addressed to external universities adhering to the open learning paradigm and companies that want to have a close connection to academia. In addition, the project results can also improve the knowledge management within the participating organisations. Questions related to processes for creation of knowledge and knowledge repositories have been placed at a strategic level, also within the enterprise, including Public Authorities with the possibility to enlarge their vision and perspective in the Academic context and in the European level.

USAGE (Urban Data Space for Green Deal) aims to provide solutions and mechanisms for making city-level environmental and climate data available to everyone based on FAIR principles. USAGE will support the implementation of the European strategy for data and various European Green Deal priority actions at the level where climate change is mostly felt: cities and towns. USAGE will provide innovative governance mechanisms, consolidated arrangements, AI-based tools and data analytics to share, access and use city-level data from Earth Observation (EO), Internet of Things (IoT), authoritative and crowd sources, leveraging on standards for data and service interoperability. USAGE wants to become a decentralized infrastructure for trustworthy data collection, processing and exchange based on commonly agreed principles, facilitating the combination of heterogeneous data for policy analysis. USAGE will validate its solutions in four diverse pilot areas located in four different countries, focusing also on the reusability of the solutions in other urban areas. The consortium consists of 11 interdisciplinary partners from 5 European countries and, within the 3 years of activities, will also realize a long-term sustainability and growth strategy plan of project solutions.

EO4GEO – “Towards an innovative strategy for skills development and capacity building in the space geo-information sector supporting Copernicus User Uptake”. EO4GEO is an Erasmus+ Sector Skills Alliance gathering 26 partners from 12 countries from academia, private and public sector active in the education/training and space/geospatial sectors. EO4GEO aims to help bridging the skills gap between supply and demand of education and training in the space/geospatial sector by reinforcing the existing ecosystem and fostering the uptake and integration of space/geospatial data and services in end-user applications. EO4GEO will work in an multi- and interdisciplinary way and apply innovative solutions for its education and training actions including: case based and collaborative learning scenarios; learning-while-doing in a living lab environment; on-the-job training; the co-creation of knowledge, skills and competencies; etc. EO4GEO will define a long-term and sustainable strategy to fill the gap between supply of and demand for space/geospatial education and training taking into account the current and expected technological and non-technological developments in the space/geospatial and related sectors (e.g. ICT). The strategy will be implemented by: creating and maintaining an ontology-based Body of Knowledge for the space/geospatial sector based on previous efforts; developing and integrating a dynamic collaborative platform with associated tools; designing and developing a series of curricula and a rich portfolio of training modules directly usable in the context of Copernicus and other relevant programmes and conducting a series of training actions for a selected set of scenario’s in three sub-sectors - integrated applications, smart cities and climate change to test and validate the approach. Finally a long-term Action Plan will be developed and endorsed to roll-out and sustain the proposed solutions.

The core objective of FAIRiCUBE is to enable players from beyond classic Earth Observation (EO) domains to provide, access, process, and share gridded data and algorithms in a FAIR and TRUSTable manner. To reach this objective, we propose creating the FAIRiCUBE HUB, a crosscutting platform and framework for data ingestion, provision, analysis, processing, and dissemination, to unleash the potential of environmental, biodiversity and climate data through dedicated European data spaces. Within this project, TRL 7 will be attained, together with the necessary governance aspects to assure continued maintenance of the FAIRiCUBE HUB beyond the project lifespan. This projects goal is to leverage the power of Machine Learning (ML) operating on multi-thematic datacubes for a broader range of governance and research institutions from diverse fields, who at present cannot easily access and utilize these potent resources. Selected use cases will illustrate how data-driven projects can benefit from cube formats, infrastructure, and computational benefits. They will guide us in creating a user-friendly FAIRiCUBE HUB, which is tightly integrated to the common European data spaces, providing relevant stakeholders an overview of both data and processing modules readily available to be applied to these data sources. Tools enabling users not intimately familiar with the worlds of EO and ML to scope the requirements and costs of their desired analyses will be implemented, easing uptake of these resources by a broader community. The FAIR sharing of results with the community will be fostered by providing easy to use tools and workflows directly in the FAIRiCUBE HUB.