The Department of Mathematical Modeling and Data Analysis at National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” has more than ten years’ experience in the area of geospatial data analysis and usage and as for now is a leading Ukrainian organization in Research & Development of methods and information technologies, and delivery of corresponding services in the Earth observation domain.
The specialists from MMDA NTUU KPI have strong expertise in the different areas of remote sensing, GIS, EO services, geospatial data fusion, ICT and cloud computing using the modern vendors like Amazon Web Services, CREODIAS, Google Cloud, Google Earth Engine, machine and, in particular, deep learning approach, pattern recognition, high-performance computing, education & training. MMDA NTUU KPI is taking part in joint GEO activities and HORIZON Europe scientific and innovation programs. The staff of the MMDA NTUU KPI are experts of the International Bank for Reconstruction and Development (“World Bank”) in Ukraine since 2016 and the Joint Research Centre of the European Commission since 2014 (geospatial statistical analysis, geospatial intelligence using cloud services).
Can you tell us your role in the project and how your expertise contributes to the project’s mission?
With longstanding expertise at the intersection of satellite remote sensing and artificial intelligence, the MMDA NTUU KPI brings advanced solutions to real-world environmental challenges.
As a part of iMERMAID, we focus on two key areas: water quality monitoring and oil spill detection in the Mediterranean Sea near Cyprus. In the oil spill detection task, we have been developing deep learning models based on transfer learning techniques, using SAR satellite imagery. By applying and adapting the LinkNet segmentation architecture, we address the scarcity of labelled Mediterranean data and enhance detection reliability through inventive pre-processing methods. This work directly supports the project’s mission to improve early detection and monitoring of hazardous marine pollution, enabling faster response and better protection of vulnerable ecosystems.
To monitor chlorophyll-a concentrations, we use global satellite products from Aqua MODIS, GCOM-C, and Sentinel-3, applying machine learning to enhance their spatial resolution and improve their consistency with ground-based measurements. This approach aligns with the project’s mission to achieve better integration between satellite data and local water sensor observations.
What do you think are the main benefits that the project brings forward, and in which areas do you envision the project having its biggest impact?
The iMERMAID project delivers practical solutions for detecting and monitoring marine pollution in the Mediterranean Sea by combining satellite data, AI, and local observations. Its biggest impact is improving early warning systems for oil spills and enhancing the precision of water quality assessments. The innovative, scalable tools directly support faster environmental response, better policy decisions, ecosystem resilience, and safeguard public health and coastal economies from long-term pollution risks.
If you had to describe the iMERMAID project in one word, what would that be?
Awareness – iMERMAID empowers us to detect what’s otherwise invisible – bringing early awareness of environmental threats from space to sea level. Awareness is the first step toward meaningful action and protection.
