Ocean watch

The Oil Spill Problem

Globally, over 10,000 billion tonne miles of crude oil are transported across the seas annually, and accidents contribute to an estimated 5,000 tons spilled each year. Oil pollution poses a constant threat to marine life such as whales, dolphins, and seabirds. These incidents often impact marine protected areas, which are vital for conserving rare habitats and species.

Smaller countries and agencies lack the resources for effective oil spill monitoring and detection. Recent incidents like the oil spill off the coast of Trinidad and Tobago in February, 2024 highlight the urgent need for early detection and response measures to mitigate the devastating impact of oil spills on marine ecosystems and coastal communities.

Our project aims to develop a swift and automated oil spill detection system using Synthetic Aperture Radar (SAR) imagery and advanced machine learning techniques. By leveraging SAR technology and cutting-edge machine learning algorithms, we seek to enable timely and accurate detection of oil spills. This solution will facilitate precise response efforts, ultimately contributing to the preservation and protection of invaluable marine ecosystems.

Motivation

On February 3rd, 2024, an unknown oil barge capsized about 150 meters off shore of Trinidad and Tobago. It was not discovered until 4 days later. The Trinidad and Tobago prime minister declared a state of emergency on February 11th as the oil leaking from the capsized vessel had already affected nearly 10 miles of coastline. By February 14th, the oil spill had spread over 100 km away. Later on February 27th, the oil spill had spread as far west as the Caribbean island of Bonaire, posing a threat to its mangrove, fish, and coral ecosystems.

This incident underscores the critical importance of early detection and rapid response mechanisms in mitigating the devastating impact of oil spills on marine ecosystems and coastal communities.

OceanWatch

The Oil Spill Problem

Motivation