A near real-time oil spill detection and forecasting Ocean Twin
Creators: name: Outmani, Sabrina
name: Spanoudaki, Katerina
name: Fazzini, Noemi
name: Vettorello, Laura
name: Ratta, Riccardo
name: FOLEGANI, marco
name: Kampanis, Nikolaos
name: Quarta, Maria Luisa
Publisher: name: Zenodo
Year: 2024
Subjects: subject: Oil spill
subject: Blue economy
subject: Digital Twin
subject: Sentinel-1
subject: Machine learning
subject: Convolutional model

description: The EU funded Iliad project builds on the assets resulting from two decades of investments in policies and infrastructures for the blue economy, aiming to establish an interoperable, data-intensive Digital Twin of the Ocean. Iliad combines ocean observations, state-of-the-art forecast models, citizen science, AI and advanced computing infrastructures to create high-resolution, multi-dimensional, near real-time virtual representations of the ocean. Several Digital Twin (DT) pilots will be undertaken inkey thematic areas such as marine renewables, biodiversity, fisheries and aquaculture and marine pollution. The current work presents the Oil Spill DT developed in the framework of Iliad. The DT focuses on Cretan Sea and aims to provide early detection of marine oil spills and operational forecasting of spill trajectories to support immediate response to pollution events, minimizing thus environmental and economic impact.A near real-time automatic oil spill detection system was developed to enable early detection of oil spills from Sentinel-1 SAR images. The approach includes statistical and ML techniques for object detection and semantic segmentation. Input data consist of 10m-resolution dual-polarization Sentinel-1 images acquired with the Interferometric Wide Swath (IW) Ground Range Detected mode at high resolution (GRD-HD). Images are automatically pre-processed in their VV polarization using customized functions based on SNAPpy. A trained Fully Convolutional One-Stage (FCOS) model is employed for object detection, fine-tuned with a dataset of over 1000 SAR images with four distinct classes: oil spill, look-alike, ship, and land [1]. Finally, a U-Net model, trained on the same dataset, is employed for the segmentation of the detected oil spill regions. The accuracy of predictions on new images heavily relies on the preprocessing steps, as the analysed images must be comparable to those utilized for the training dataset. Oil spill segmentation can be also performed via statistical approaches with good results: the workflow allows for the employment of a SNAPpy-based adaptive thresholding algorithm for oil spill delineation. Oil spill detection triggers MEDSLIK-II and OpenDrift models for predicting the fate and transport of the spills, which are coupled to operational, high-resolution met-ocean forecasts.The oil spill detection and forecasting system is tested by reconstructing the Ulysse-Virginia oil spill, which occurred off the coast of Corsica on October 7th, 2018. Following the spill detection and mask delineation, the two numerical models are initiated for simulating the oil spill fate and transport. Quantitative metrics are used to evaluate the ability of the oil spill models to reproduce the satellite observations.