DISCOVER aims to develop an autonomous, synchronous, continuous and intelligent identification and data analysis system for materials and products in existing end-of-life built works. The proposed approach will provide key stakeholders, including academia research performers, along with construction industry representatives, with data-driven insights to make deconstruction more efficient, optimise the use of resources, improve the environmental footprints and enhance the circularity of construction and demolition, unlocking the potential of end-of-life built works, which will become material banks. The expected outcomes include an autonomous robotic platform coupled with continuous identification tools to scan built works and provide synchronous quantitative and qualitative data from different materials, including complex and concealed elements. Artificial intelligence algorithms will allow a rapid analysis of the properties and characteristics of components, and feed the automated scan-to-BIM model creation. The multi-dimensional BIM, including selective demolition processes, labour productivity, and technical planning, will become a Digital Twin of the demolition site optimised by social, economic, and environmental multi-criteria assessments. This approach will highly contribute to significantly increasing the supply of traceable and sustainable construction materials and products for enhancing their wider market acceptance, following the waste hierarchy. The social impacts of digital transformation in the construction sector will be considered, and also new professional development tools for the relevant stakeholders will be proposed. The system will be tested in 4 different real demolition sites (Spain, Portugal, Poland and Belgium), offering a complete range of built work typologies and wide geographical coverage to demonstrate the replicability potential of DISCOVER, increasing the project dissemination capacity and awareness among the construction sector.

The SCENE project has been majorly designed to offer the means for a modern & globally competitive, European filmmaking industry, by building upon two solid pillars, i.e. semantically cognitive AI technologies and the (in) tangible European Cultural assets, always with respect to the European values & policies regarding the human and its environment. SCENE will develop a wide set of cutting-edge technologies & services that are able to cover the whole pipeline of the film-making industry, “from farm to fork”, i.e. from the very early stages of the film planning until their playback at the audience screen. Specifically, SCENE aims to innovate by enriching the existing data lakes with high-qualitative & editable 3D digital models of European cultural sites, and by enhancing their accessibility with the integration of multi-dimensional knowledge graphs carrying location- & cultural-aware information. SCENE will further facilitate the film-making & film-editing side during the production & the post-production phase with efficient simulation & novel assessment mechanisms. Similarly, it will enable a smart & privacy-preserving interaction channels between industrial stakeholders & the audience, that will allow not only for pro-active sensing of the audience’s preferences in the pre-production phase, but also for the early identification of the most matching advertisement & distribution channels during the distribution phase, as well as the insightful matching & recommendation between films & individuals. The integration of the aforementioned technologies under the unique SCENE platform demonstrates significant capacity to render an excellent proof-of-concept prototype to guide, inspire and mark significant breakthrough in the film making industry during the post COVID era, on scientific, commercial, business & policy level.

The digital transformation and the availability of more diverse and cost-effective methods for 3D capture has led to the creation of digitised representations of parts of our public spaces, machinery and processes, so-called Digital Twins. These Digital Twins are an important basis for building advanced XR applications for cityscapes and the industrial environments. These XR applications need high-fidelity models for accurate localization, which are kept in-sync with the real world. However, currently (i) creating high-fidelity Digital Twins is costly, (ii) integrating data from heterogeneous sensors is still challenging and (iii) the update of Digital Twins requires explicit manual intervention. The vision of DIDYMOS-XR is to enable advanced, more realistic and more dynamic extended reality (XR) applications, powered through artificial intelligence. The project thus focuses on advancing technologies for creating large-scale digital twins, synchronised with the real world. DIDYMOS-XR will research and develop methods for reconstruction and mapping from heterogeneous inputs, including static and mobile sensors, AI-based data fusion, scene understanding and rendering. Digtal twin based XR applications also require means of accurately positioning in the environment, aware of the dynamics of the scene, also addresed by the project. The capture of scenes at scale, as well as using cameras and other sensor data for synchronising the digital representation, bears the risk of capturing personal and sensitive data. Hence, the technologies resulting from DIDYMOS-XR must be and will be ethical and privacy-aware by design. In order to demonstrate and validate the enabling technologies researched and developed in DIDYMOS-XR, the project will address use cases of XR applications in two domains that differ in their scale and characteristics. One is urban planning and smart mobility, the other is the collaboration with autonomous mobile robots at an industrial production facility.

Ashvin aims at enabling the European construction industry to significantly improve its productivity, while reducing cost and ensuring absolutely safe work conditions, by providing a proposal for a European wide digital twin standard, an open source digital twin platform integrating IoT and image technologies, and a set of tools and demonstrated procedures to apply the platform and the standard proven to guarantee specified productivity, cost, and safety improvements. The envisioned platform will provide a digital representation of the construction product at hand and allow to collect real-time digital data before, during, and after production of the product to continuously monitor changes in the environment and within the production process. Based on the platform, Ashvin will develop and demonstrate applications that use the digital twin data. These applications will allow it to fully leverage the potential of the IoT based digital twin platform to reach the expected impacts (better scheduling forecast by 20%; better allocation of resources and optimization of equipment usage; reduced number of accidents; reduction of construction projects). The Ashvin solutions will overcome worker protection and privacy issues that come with the tracking of construction activities, provide means to fuse video data and sensor data, integrate geo-monitoring data, provide multi-physics simulation methods for digital representing the behavior of a product (not only its shape), provide evidence based engineering methods to design for productivity and safety, provide 4D simulation and visualization methods of construction processes, and develop a lean planning process supported by real-time data. All innovations will be demonstrated on 10 real-world construction projects across Europe. The Ashvin consortium combines strong R&I players from 11 EU member states with strong expertise in construction and engineering management, digital twin technology, IoT, and data security / privacy.

The ENERGENIUS project provides an innovative pathway dedicated to cultivating an inclusive, community-driven energy transition, underpinned by the ethos of 'Leave No One Behind.' At its heart is the ENERGENIUS Suite, a comprehensive, one-stop-shop digital platform providing a wide range of interlinked services, including digital twin models, serious games, cross-sectoral dashboards, marketplace and AI-driven recommendations. This robust ecosystem amalgamates data across various sectors—from energy, healthcare, and finance to traffic, social media, real estate, and tourism—delivering customized, AI-powered solutions for energy efficiency. By harnessing the collective intelligence of its community, ENERGENIUS not only offers personalized energy-saving advice but also reciprocally enriches multiple sectors. Upholding the highest standards in interoperability and privacy-by-design, ENERGENIUS is establishing new milestones for secure, seamless cross-sectoral data exchange.