Oil Spill Detection

This use case wants to enhance the existing oil spill monitoring and forecasting system, OKEANOS, by establishing an operational service on the iMagine platform. This service will utilize satellite imagery to automatically detect oil spills and provide more accurate and localized oil spill forecasts. The technology behind OKEANOS relies on open and quality-controlled inputs such as meteo-oceanographic fields, bathymetry, and coastline geometry, as well as satellite imagery from the Sentinel 1, 2, and 3 constellations.

One of the challenges in oil spill forecasting is the lack of quantification of uncertainties, which stems from the absence of quality-controlled observations and well-established validation methods. The accuracy of oil spill forecasts is also affected by the limitations of ocean and atmospheric models in reproducing small-scale features. Implementing high-resolution meteo-oceanographic models to improve forecast accuracy is expensive and time-consuming. Therefore, this use case aims to leverage the AI platform to address these challenges by improving the algorithms for automatic oil spill detection and classification using satellite imagery and enhancing the accuracy of numerical oil spill forecasts.

The development timeline for this use case involves improving AI/ML algorithms, optimizing workflows, organizing existing data into downloadable collections, and working on high-resolution inputs and accurate forecasts. The accuracy of the forecasts will be quantified, and the data repository of marine products will be prepared in accordance with FAIR principles for easy search and discovery. The use case plans to release two versions of the services before reaching the milestone at 24 months.