Periodic Reporting for period 1 - AI-OBSERVER (Enhancing Earth Observation capabilities of the Eratosthenes Centre of Excellence on Disaster Risk Reduction through Artificial Intelligence)
Reporting period: 2022-10-01 to 2023-12-31
AI-OBSERVER project aims to significantly strengthen and stimulate the scientific excellence and innovation capacity, as well as the research management and administrative skills of the ERATOSTHENES Centre of Excellence (ECoE), through several capacity building activities on AI for EO applications in the Disaster Risk Reduction thematic area, upgrading and modernising its existing department of Resilient Society, as well as its research management and administration departments, and assisting the ECoE to reach its long-term objective of raised excellence on AI for EO on environmental hazards. A close and strategic partnership between the ECoE (Cyprus) and two internationally top-class leading research institutions, the German Research Centre for Artificial Intelligence (DFKI) from Germany and the University of Rome Tor Vergata (UNITOV) from Italy, will be achieved, leading to a research exploratory project on the application of AI on EO for multi-hazard monitoring and assessment in Cyprus. Moreover, CELLOCK Ltd, the project’s industrial partner, will lead the commercialisation, exploitation and product development aspects of AI-OBSERVER and its exploratory project outputs. All outputs will be disseminated and communicated to stakeholders, the research community, and the public, assisting the ECoE to accomplish its exploitation goals, by creating strong links with various stakeholders from academia and industry in Cyprus and beyond, that ECoE will capitalise on, long after the end of the project.
During Reporting Period 1 (RP1), the deliverables of WP1, i.e. D1.1: Data Management Plan (DMP); D1.2: Risk assessment and contingency plan; and D1.3: Quality Assurance plan were submitted. The DMP presents the project's data management policy and describes the framework for accessing, using, and re-using data generated and collected. D1.2 describes potential risks and the corresponding mitigation actions, leading to the development of a risk assessment matrix, that will be systematically updated until the end of the project. D1.3 presents the Quality Assurance Plan (QAP) outlining procedures for maintaining quality throughout the project, and specifies implementation procedures, including guidelines for personnel, research, data management, and communication. Moreover, four project coordination meetings took place, including the project's kick-off, achieving Milestone 1, along with online meetings when needed for the coordination of the consortium's activities/tasks.
Furthermore, deliverables D2.1: Current and emerging trends on AI for EO related research; D2.2: Stakeholders’ database; and D2.3: Strategic collaboration plan and five-year implementation roadmap for future collaborations were submitted. D2.1 was 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, and predict any future changes in AI technologies relevant to EO applications. D2.2 presents a database that contains potential stakeholders in the thematic area of EO for DRR using AI, with 267 organisations found relevant to the research theme categories. Last but not least, a Strategic collaboration plan and five-year implementation roadmap for future collaborations (D2.3) was delivered, aiming to establish research ties with the identified stakeholders, transforming them into potential partners for funding prospects and knowledge transfer activities.
In WP3, D3.1: Report on the evaluation of the ECoE’s current position; and D3.2: Gap analysis report were delivered during RP1, achieving MS4: Gap analysis of the AI-OBSERVER project. D3.1 assessed the current status of the ECoE in terms of scientific excellence and innovation capacity, that concluded with a SWOT analysis. The evaluation was performed through baseline analysis based on selected aspects, characterised by indicators that will provide an insight on the aspect’s performance. 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.
Finally, numerous capacity building and knowledge transfer activities took place in the context of WP4. In fact, DFKI and UNITOV organised and carried out 2 AI-related workshops (AI-WS1 & AI-WS2), 2 AI-related webinars (AI-WEB1 & AI-WEB2), 1 short-term staff exchange (SSE1), 1 summer school (AI-JSS1), 2 webinars (RMA-WEb1 & RMA-WEB2) on Research Management and Administration (RMA) and 1 Expert Visit on RMA (RMA-EV1) achieving MS5: Capacity building activities – 1st year. 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. This collaboration has already led to one open-access journal publication, the particpation in an international conference, with 3 more conference publications in progress and the submission of research proposals on AI-related topics.
Furthermore, deliverables D2.1: Current and emerging trends on AI for EO related research; D2.2: Stakeholders’ database; and D2.3: Strategic collaboration plan and five-year implementation roadmap for future collaborations were submitted. D2.1 was 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, and predict any future changes in AI technologies relevant to EO applications. D2.2 presents a database that contains potential stakeholders in the thematic area of EO for DRR using AI, with 267 organisations found relevant to the research theme categories. Last but not least, a Strategic collaboration plan and five-year implementation roadmap for future collaborations (D2.3) was delivered, aiming to establish research ties with the identified stakeholders, transforming them into potential partners for funding prospects and knowledge transfer activities.
In WP3, D3.1: Report on the evaluation of the ECoE’s current position; and D3.2: Gap analysis report were delivered during RP1, achieving MS4: Gap analysis of the AI-OBSERVER project. D3.1 assessed the current status of the ECoE in terms of scientific excellence and innovation capacity, that concluded with a SWOT analysis. The evaluation was performed through baseline analysis based on selected aspects, characterised by indicators that will provide an insight on the aspect’s performance. 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.
Finally, numerous capacity building and knowledge transfer activities took place in the context of WP4. In fact, DFKI and UNITOV organised and carried out 2 AI-related workshops (AI-WS1 & AI-WS2), 2 AI-related webinars (AI-WEB1 & AI-WEB2), 1 short-term staff exchange (SSE1), 1 summer school (AI-JSS1), 2 webinars (RMA-WEb1 & RMA-WEB2) on Research Management and Administration (RMA) and 1 Expert Visit on RMA (RMA-EV1) achieving MS5: Capacity building activities – 1st year. 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. This collaboration has already led to one open-access journal publication, the particpation in an international conference, with 3 more conference publications in progress and the submission of research proposals on AI-related topics.
The AI-OBSERVER project is expected to have scientific (SC), economic (E), and societal impact (S), which is monitored through a set of Key Performance Indicators as follows:
- SC01: Capacity building and training activities in the form of workshops, webinars, and short staff exchanges.
- SC02: Facilitate networking with top class institutions/ stakeholders in the fields of AI and EO in the EMMENA region and beyond.
- SC03: Strengthen the research management and administration skills of the ECoE.
- E01: Creation of new research positions in the area of Artificial Intelligence for EO.
- E02: Exploitation of AI-OBSERVER results by relevant authorities/ stakeholders.
- E03: Research/ Consultancy funding.
- E04: Provision of new innovative products/services.
- S01: Raising information awareness to the public.
- S02: Raising information awareness to stakeholders, interested parties and the public.
- S03: Attracting top researchers and students in the area of Artificial Intelligence for EO.
- S04: Number of women scientists and their roles in the research.
During the RP1, several activities to maximise the project's impact and the further exploitation of its outcomes were carried out, such as TV appearances, newspaper articles, social media posts (LinkedIn, Facebook, X), 4 meetings with stakeholders, public lectures, attendance at the European Researcher's night, journal publications, conference participation. Moreover, through the capacity building activities carried out by the advanced partners, the impact to the project's coordinator (ECoE) and its staff is also accomplished. In this direction, other key measures to ensure further uptake and success of the project include but are not limited to: (1) the continuous engagement of stakeholders and end-users throughout the project; (2) Co-design and co-develop of the exploratory research project outputs; (3) extension of the collaboration of the coordinator with the advanced partners in terms of joint publications, research proposals and development of services.
- SC01: Capacity building and training activities in the form of workshops, webinars, and short staff exchanges.
- SC02: Facilitate networking with top class institutions/ stakeholders in the fields of AI and EO in the EMMENA region and beyond.
- SC03: Strengthen the research management and administration skills of the ECoE.
- E01: Creation of new research positions in the area of Artificial Intelligence for EO.
- E02: Exploitation of AI-OBSERVER results by relevant authorities/ stakeholders.
- E03: Research/ Consultancy funding.
- E04: Provision of new innovative products/services.
- S01: Raising information awareness to the public.
- S02: Raising information awareness to stakeholders, interested parties and the public.
- S03: Attracting top researchers and students in the area of Artificial Intelligence for EO.
- S04: Number of women scientists and their roles in the research.
During the RP1, several activities to maximise the project's impact and the further exploitation of its outcomes were carried out, such as TV appearances, newspaper articles, social media posts (LinkedIn, Facebook, X), 4 meetings with stakeholders, public lectures, attendance at the European Researcher's night, journal publications, conference participation. Moreover, through the capacity building activities carried out by the advanced partners, the impact to the project's coordinator (ECoE) and its staff is also accomplished. In this direction, other key measures to ensure further uptake and success of the project include but are not limited to: (1) the continuous engagement of stakeholders and end-users throughout the project; (2) Co-design and co-develop of the exploratory research project outputs; (3) extension of the collaboration of the coordinator with the advanced partners in terms of joint publications, research proposals and development of services.