During the AI-OBSERVER project, the following deliverables were completed. The DMP (D1.1) presents the project's data management policy and describes the framework for accessing, using, and re-using data generated and collected. The Risk assessment and contingency plan (D1.2) describes potential risks and the corresponding mitigation actions, that will be systematically updated until the end of the project. The Quality Assurance plan (D1.3) outlined procedures for maintaining quality throughout the project, and specifying implementation procedures, including guidelines for personnel, research, data management, and communication. D2.1 provided an extensive state-of-the-art review on the current and emerging trends in AI to identify the best practices for the integration of AI with the research area of Disaster Risk Reduction (DRR) of the ECoE. D2.2 presented a database that contains potential stakeholders in the thematic area of EO for DRR using AI, with 267 organizations found relevant to the research theme categories. Α Strategic collaboration plan and five-year implementation roadmap for future collaborations (D2.3) aimed to establish research ties with the identified stakeholders, transforming them into potential partners for funding prospects and knowledge transfer activities. D3.1 assessed the current status of the ECoE in terms of scientific excellence and innovation capacity, that concluded with a SWOT analysis. A Gap analysis (D3.2) was also performed to identify the ECoE’s staff needs and the activities necessary for capacity building after the integration of advanced Al technologies in their DRR related EO activities. The target position was to establish ECoE as a key player in the integrated area of EO using AI technologies, which is presupposed that ECoE is viable and sustainable in the long term.
Moreover, 23 capacity building and knowledge transfer activities took place in the context of WP4. DFKI and UNITOV organized and carried out 6 AI-related workshops (AI-WS1-6), 4 AI-related webinars (AI-WEB1-4), 3 short-term staff exchange (SSE1-3), 2 summer schools (AI-JSS1-2), 2 expert visits (AI-EV1-2); and 1 workshop (RMA-WS), 4 webinars (RMA-WEB1-4) and 1 Expert Visit (RMA-EV1) on Research Management and Administration (RMA). These provided useful knowledge and skills towards achieving the project's goals to strengthen the scientific excellence and innovation capacity of ECoE on EO through AI, increase the inwards and outwards mobility of qualified scientists, and enhance the RMA skills of the ECoE staff. The presentations, training material and recordings from all capacity building activities are stored in a dedicated Microsoft SharePoint, as a source of knowledge that is systematically exploited by ERATOSTHENES CoE staff. The WP4 related materials, results and outputs are documented in D4.1 D4.2 D4.3 and D4.4.
The knowledge transferred by the advance partners to ERATOSTHENES CoE personnel was applied and showcased in a research exploratory project with the end-result being the development of six risk assessment/monitoring models for earthquakes, landslides, coastal erosion, fires, floods and marine pollution, using AI and EO (D5.3). Among other steps, the collection of user requirements and data for the development of these models (D5.1) the development of the Big Data Management platform (D5.2) to visualize the models' outputs, and the roadmap towards commercialization (D5.4) that set the foundation and steps that need to be taken for the commercialization of the AI-OBSERVER project's products, were also conducted.
