Short name: iMERMAID
Name: “Innovative solutions for Mediterranean Ecosystem Remediation via Monitoring and decontamination from Chemical Pollution”
Instrument (Call): HORIZON-MISS-2022-OCEAN-01
Duration: 3 years (2023 – 2025)
Abstract: The Mediterranean Sea and its surrounding regions support a diverse variety of essential socioeconomic activities. It is one of the highly exploited water ways and the influence of anthropogenic activities on its marine habitats and ecosystems has grown significantly since the industrial revolution. Because of this, the Mediterranean Sea basin is very vulnerable to chemical contamination and buildup. To safeguard the Mediterranean Sea basin from contaminants for emerging concerns (CoEC), iMERMAID will integrate, coordinate, and synergize innovative preventive, monitoring, and remediation solutions. MERMAID will build an evidence-based multidimensional framework that will guide policymaking and transform societal perceptions to reduce CoEC usage, emissions, and pollution. Furthermore, next generation sensor and remediation solutions will be developed within iMERMAID to monitor and remove prioritized chemicals from its source while reducing upstream pollution. iMERMAID builds an ideal interdisciplinary team by bringing together prominent SMEs, researchers, regulators, and innovation professionals who have been essential in improving the knowledge and awareness of CoEC. Beyond state-of-the-art techniques, iMERMAID will strive to strengthen regulations against CoEC, expand economic possibilities and competitiveness, improve the standard of living for EU residents, while preventing the accumulation of chemical pollution in the Mediterranean Sea basin. iMERMAID will empower the efforts to create a zero pollution, contaminant free waters by enabling the Chemical Strategy’s goals to become a practical reality.
Publications
- Kussul Nataliia, Kuzin Volodymyr, Salii Yevhenii, Yailymov Bohdan, Shelestov Andrii Transfer Learning Models for Oil Spills Detection Based on Satellite Data. 4th International Symposium on Applied Geoinformatics (ISAG2024), May 9 – 10, 2024, 15252. Link>>>
- Henitsoi Pavlo, Shelestov Andrii Transfer Learning Model for Chlorophyll-a Estimation Using Satellite Imagery. 4th International Symposium on Applied Geoinformatics (ISAG2024), May 9 – 10, 2024, 15247. Link>>>
- Nataliia Kussul, Volodymyr Kuzin, Yevhenii Salii, Bohdan Yailymov, Andrii Shelestov Transfer learning approach for oil spills’ detection using SAR satellite data. 2024 IEEE 42nd International Conference on Electronics and Nanotechnology (ELNANO), May 13 – 16, 2024 (presented)
- Bohdan Yailymov, Pavlo Henitsoi, Nataliia Kussul, Andrii Shelestov Increasing Chlorophyll-A Spatial Resolution Using Machine Learning. IEEE International Conference on System Analysis & Intelligent Computing (SAIC), October 08 – 11, 2024 (presented)
- Bohdan Yailymov, Nataliia Kussul, Pavlo Henitsoi, Andrii Shelestov Improving spatial resolution of chlorophyll-a in the Mediterranean sea based on machine learning. Journal Radioelectronic and Computer Systems. Vol 2024, No 2 (2024), p. 52-65. https://doi.org/10.32620/reks.2024.2.05
- Nataliia Kussul, Volodymyr Kuzin, Yevhenii Salii, Bohdan Yailymov, Andrii Shelestov Single-Polarized SAR Image Preprocessing in Scope of Transfer Learning for Oil Spill Detection. 2024 IEEE 12th International Conference on Intelligent Systems (IS), Varna, Bulgaria, August 29-31, 2024. https://doi.org/10.1109/IS61756.2024.10705228
- Andrii Shelestov, Pavlo Henitsoi, Bohdan Yailymov, Nataliia Kussul Advanced pollutions’ monitoring in the Mediterranean Sea: AI-based approach using satellite data and products. Springer Lecture Notes in Networks and Systems (chapter accepted).
- A. Shelestov, N. Kussul, Y. Salii, V. Kuzin, B. Yailymov, S. Drozd, P. Henitsoi Earth observations in HORIZON Europe iMERMAID project. Poster presentation on EuroGEO Workshop 2024, 8-10 October 2024, Krakow Poland.
- S. Drozd, N. Kussul Downscaling Aqua MODIS and GCOM-C Data for Enhanced Chlorophyll-a Monitoring in Cyprus’ Coastal Waters with Sentinel-3 and Machine Learning. Science Of Remote Sensing (on reviewing).