Recent advances in the fields of electronics and optics technology have permitted the design and development of sophisticated hyperspectral imaging sensors, which are able to capture the naturally occurring imaging spectra at a very high spatial resolution forming three-dimensional data cubes. In addition, it is envisaged that the next generation hyperspectral video cameras will have the ability to capture several hyperspectral data cubes per second, at almost video rates. Hyperspectral video sequences possessing high temporal, spatial, and spectral resolution will combine the advantages of both video and hyperspectral imagery. This unprecedented wealth of information poses a major challenge and necessitates the development of highly sophisticated signal processing systems. Addressing simultaneously the explosive growth of data dimensionality and the need to accurately determine the type and nature of the objects being imaged is a task that is not sufficiently treated currently by conventional statistical data analysis methods. The objective of this project is to develop, test, and evaluate novel signal processing technologies for real-time processing of hyperspectral data cubes. Although hyperspectral sensors capture massive amounts of high-dimensional data, relevant information usually lies in a low-dimensional space. Our aim is to extend recent theoretical and algorithmic developments in the field of sparsity-enforcing recovery, compressive sensing, and matrix completion, in order to build and exploit sparse representations adapted to the hyperspectral signals of interest. It is envisaged that all three, temporal, spatial and spectral domains of hyperspectral data will be explored for sparse representations. Thus, sparsity in the data will be used not only to improve estimation performance, but also to mitigate the enormous computational burden needed to analyze hyperspectral data and leverage the development of real-time hyperspectral processing systems.

The Copernicus program is today at a cornerstone: •The Sentinel satellites are being deployed. Their images, associated with Third Party missions’ data are being used for delivering Copernicus core services results (at global, European, and regional levels). •The European structures (EEA, ECMWF, EMSA , etc.) and the scientific community are starting to use operationally these data and the results for a better knowledge and understanding of the key land-cover stakes and environmental monitoring. But, the regional actors who are responsible for managing (at least partially) land-cover and natural resources policies have still difficulties to get access to these data and information, and moreover are not in position to combine them with their existing geo-information systems. A group of five SMEs (TerraNIS, Spacebel, Planetek, Terraspatium and Sertit), supported by a consulting firm specialized in Space market innovation and organization (Cap High Tech), are proposing to provide the regional institutional and commercial users with operational information services. These services will take the highest benefit from Copernicus outputs, for territory monitoring and management. These SMEs have decided to put in common their complementary skills and products, in the frame of a dedicated association (called EUGENIUS). They will implement “regional hubs” (Geo-information platforms) building the first instance of the “EUGENIUS network”. These regional hubs shall deliver services in the following domains: •Urbanization monitoring and management (densification, preservation of rural and “green” areas, transportation means, etc.) •Agriculture areas and activities (crop monitoring, crop identification and classification, potential yield assessment, water resources and irrigation, etc.) •Forest monitoring (surfaces, trees species classification, exploitation status, etc.) •assessing and monitoring some natural risks at regional levels (flooding, landslides and water quality)

Technological advances in remote sensing have increased the availability of satellite images with different spatiotemporal and spectral characteristics.There is difficulty for retrieving the most appropriate data for each user's needs.One key challenge is to connect the quantitative information of the EO images with the qualitative (high-level user queries) and be able to mine these connections in big archives.An inherent question arises; how to retrieve EO images based on user semantically aware questions.Content based EO image retrieval techniques have been introduced for bridging the gap between low-level image features and high-level queries.The main constraint of the existing approaches is the generalization of the problem.The formulated ontologies are not focused on the constraints of EO images.The main objective of SEO-DWARF is to realize the content-based search of EO images on an application specific basis.The marine application domain and data from Sentinels 1,2,3, ENVISAT will be used.Queries such as “Calculate the rate of increasing chlorophyll in the NATURA area” will be answered by the SEO-DWARF, helping users to retrieve the appropriate EO images for their specific needs or alert them when a specific phenomenon occurs.The research contains the:a) ontology formalization for the specific research topics,b) determination of the semantic queries for the application domains,c) algorithm development for extracting metadata from the EO images,d) design of an architecture of the platform to perform the semantic image retrieval and storage and management of the extracted metadata.All four aspects will be integrated in an innovative and user-friendly web based platform enabling the users to retrieve images for marine applications or register for a semantic alert.A strong and experienced research team, of 4 academic and 5 industrial partners, coming from Greece(3), Italy(2), Germany(1), France(1), Cyprus(1) and Switzerland(1) constitute the project’s consortium.

AI:Liner will enhance the asset management life cycle of sewer networks by designing, developing and validating a suite of cutting-edge technological and digital solutions. The project delivers a modular and interoperable end-to-end asset management tool chain that optimizes planning, operations, maintenance, investment, and reduces costs and risks of unexpected failures. It leverages the momentum of European utilities' digitalization efforts, building on the increasing availability of CCTV inspection data, AI-based fault detection, and recent advances in in-situ monitoring techniques to support the climate-neutral and resilient transformation of Europe's sewer infrastructure. AI:Liner empowers utilities and municipalities to identify intervention needs, enabling strategic decisions on when and where to inspect and rehabilitate sewers with the most appropriate technology, ultimately promoting the adoption of repair and rehabilitation techniques over the traditional excavation required for renewal. AI:Liner promotes the acceptance, trustworthiness and accountability of data-driven solutions, fostering a culture of trust, transparency and responsible use of data among European utilities and municipalities. By bringing together an interdisciplinary consortium that leverages expertise in infrastructure management, digitalization, and social sciences and humanities, AI:Liner enhances the digital skills of the utility workforce and integrates AI ethical principles at the core of technology development, ensuring the sustainable, responsible, and unbiased use of AI. AI:Liner strives to accelerate the digital and green transitions of the water and infrastructure sector, aiding European utilities in delivering high-quality services, maintaining affordable costs, and preserving infrastructure for future generations.