Bronze Open Access
Green Open AccessEnhancing disaster risk reduction through Artificial Intelligence: Capitalizing on the capacity building activities of the AI-OBSERVER Twinning project
Date Issued
January 1, 2024
Abstract
In the framework of the AI-OBSERVER project, the capabilities of ERATOSTHENES Centre of Excellence (CoE) on Earth Observation (EO) based Disaster Risk Reduction are significantly enhanced through a series of capacity building activities on Artificial Intelligence (AI) that are provided by the project's two advanced partners, the German Research Centre for Artificial Intelligence (DFKI) from Germany, and the University of Rome Tor Vergata (UNITOV) from Italy. These were designed, following a gap analysis of the existing staff and scientific capacity of the ERATOSTHENES CoE researchers, on the thematic research areas of: (i) Land movements (Earthquakes, Landslides and Soil erosion); (ii) Forest fires; (iii) Floods and extreme meteorological events; and (iv) Marine Pollution (oil spills, illegal waste damping, etc.). DFKI and UNITOV are transferring their scientific expertise through several workshops, webinars, short-term staff exchanges, summer schools and expert visits covering a combination of these AI-related topics, aiming to fill the identified gaps. All these will enable the ERATOSTHENES CoE researchers to build AI models for large scale image processing and Big EO data. Up to date, over thirty early stage and senior researchers have participated in these trainings. The knowledge transferred to ERATOSTHENES CoE will be utilised by its staff in a research exploratory project applying Artificial Intelligence on Earth Observation for multi-hazard monitoring and assessment in Cyprus, with the support of the advanced partners, leading to the development of the first ERATOSTHENES CoE product integrating EO and AI for Disaster Risk Reduction.
Funding
This study was carried out in the framework of AI-OBSERVER Twinning project titled “Enhancing Earth Observation
capabilities of the Eratosthenes Centre of Excellence on Disaster Risk Reduction through Artificial Intelligence” that is
funded by the European Union with Grant Agreement No. 101079468. The authors would also like to acknowledge the
'EXCELSIOR': ERATOSTHENES: Excellence Research Centre for Earth Surveillance and Space-Based Monitoring of
the Environment H2020 Widespread Teaming project (www.excelsior2020.eu). The 'EXCELSIOR' project has received
funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No.
857510, from the Government of the Republic of Cyprus through the Directorate General for the European Programmes,
Coordination and Development and the Cyprus University of Technology.
capabilities of the Eratosthenes Centre of Excellence on Disaster Risk Reduction through Artificial Intelligence” that is
funded by the European Union with Grant Agreement No. 101079468. The authors would also like to acknowledge the
'EXCELSIOR': ERATOSTHENES: Excellence Research Centre for Earth Surveillance and Space-Based Monitoring of
the Environment H2020 Widespread Teaming project (www.excelsior2020.eu). The 'EXCELSIOR' project has received
funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No.
857510, from the Government of the Republic of Cyprus through the Directorate General for the European Programmes,
Coordination and Development and the Cyprus University of Technology.
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Scopus Subject Areas
Electronic, Optical and Magnetic Materials
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Condensed Matter Physics
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Computer Science Applications
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Applied Mathematics
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Electrical and Electronic Engineering