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James Cook AI Fuses Satellite Data to Pinpoint Oil Spills from Space

James Cook University researchers developed an AI method that fuses SAR and hyperspectral satellite imagery to detect and characterize oil spills more precisely from space. The approach combines SAR's strength at mapping surface roughness with HSI's detailed spectral data to distinguish thin from thick oil. This fusion can improve response planning, help track oil movement, and be adapted to other environmental monitoring tasks.

07:20 PM, 11/25/2025Environment

Researchers at James Cook University in Australia have developed an artificial intelligence method that links space-based observations to ocean monitoring, improving the detection and characterization of oil on the sea surface.

How the system works

The team uses an AI algorithm to fuse two complementary types of satellite imagery—synthetic aperture radar (SAR) and hyperspectral imaging (HSI)—to produce high-precision oil-spill maps from orbit. SAR detects changes in sea-surface roughness (oil smooths the water surface), making it effective at outlining slicks. HSI provides detailed spectral information that helps identify the oil's composition and distinguish thin from thick layers. Combined, the two data sources deliver clearer spill boundaries and more reliable oil-type recognition than either method alone.

"This is a major step forward in oil spill detection, and it will allow us to detect spills more accurately and tell whether the oil is thick or thin,"

said lead researcher and doctoral candidate Quanwei Liu.

Why this matters

Accurate mapping of oil quantity, concentration and movement is essential for planning cleanup efforts and protecting sensitive ecosystems. Estimates suggest roughly 706 million gallons of waste oil enter the world's oceans each year, and catastrophic events can release enormous volumes — for example, the Deepwater Horizon blowout released about 206 million gallons into the Gulf of Mexico in 2010 and ultimately affected more than 43,000 cumulative square miles, according to NOAA.

"Fusing them captures the best of both — cleaner spill outlines and stronger oil-type recognition — beating other approaches,"

said project supervisor Kevin Huang.

Beyond immediate spill response, the fusion approach could be adapted to monitor a wide range of environmental issues from methane leaks to coastal pollution and habitat changes. Satellites already play important roles in environmental science, and AI-powered fusion of different sensor types can make those observations significantly more actionable.

Broader context and action

Oceans absorb the majority of the planet's excess heat — NOAA estimates about 91% — and pollution such as oil harms marine life, damages fisheries and beaches, and complicates seafood safety. Improved detection tools are one piece of a larger response that also includes prevention, regulation and investment in cleaner alternatives.

The James Cook team says the method could be applied in many environmental contexts, giving decision-makers faster, more precise information when it matters most.

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James Cook AI Fuses Satellite Data to Pinpoint Oil Spills from Space - CRBC News