Monitoring the temporal and spatial variability of offshore oil pollution, primarily caused by oil and gas infrastructure and shipping activities, is crucial for environmental protection and regulatory compliance. Satellite imagery provides a valuable source of information on oil pollution, offering global coverage. By leveraging the power of machine learning (ML), big data, and high-performance computing (HPC), oil spill monitoring is poised to take a significant leap forward.

Collaboration with dedicated AI labs and specialist remote sensing scientists enables the monitoring of global oil spills with unprecedented accuracy and speed.
Trained on a global dataset created with industry support and funding from the European Space Agency, and hosted on Viridien’s leading HPC infrastructure, algorithms are taking oil spill monitoring to the next level in terms of situational awareness. Significant technological advancements have been implemented to establish a baseline of “normal working conditions” across the world’s oceans, based on a high-cadence, high-resolution temporal archive of Earth Observation data.

Exciting developments are underway in harnessing machine learning and AI capabilities to forward and backward model spill extents at pace and scale, determining likely travel directions, and mitigating environmental risk. Here, we demonstrate the benefits of using satellite data combined with ML, HPC, and specialist algorithms to provide evidence of clean and safe operations, as well as an early warning system for unexpected releases.