Mature use case

Oil Spill Detection

Oil spill detection from satellite images


To establish an operational service at the iMagine platform for automatic processing of satellite images for detecting oil spills as extra component of an existing marine pollution oil spill monitoring and modelling service.

Development actions during iMagine


Using the iMagine platform for further refining the AI supported detection of oil spills. Training datasets containing labelled images of oil spills in satellite imagery will be used to improve existing deep learning algorithms, as well as to test advanced approaches like transfer learning between trained Deep Neural Networks. Use will be made of images from Sentinel 1, Sentinel 2 and Landsat 8 satellites with focus on Mediterranean Sea.


Setting up an operational AI service, including image database, for oil spill detection at the iMagine platform and configuring the interfacing with the existing operational marine pollution oil spill monitoring and modelling service running at CMCC


Developing guidance documentation for other researchers for using the AI service


Reaching out to other researchers and provide support and training

Objective and challenges

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.

Timeline and progress

start date
Sep 2022
end date
Aug 2025

Expected Results


Establishing operational use of satellite images for optimizing the oil spill monitoring and modelling service and its results, which will benefit its professional users. In addition, more researchers will have access and could use the AI service and database for their modelling research.

Involved Partners


lead and expert in oceanographic and oil spill modelling and forecasting


 expert in processing of satellite images, also using AI

University of Trento

expert on scientific data management (environmental domain), platform and service development