A machine learning model is scanning the oceans for the glint of garbage.
What’s new: Researchers from the UK’s Plymouth Marine Laboratory trained a model to identify ocean-borne refuse.
How it works: The European Space Agency’s two Sentinel-2 satellites capture light that reflects off the Earth’s surface. The algorithm examines this imagery, pixel by pixel, for evidence of plastic.
- Every sort of object reflects light differently, especially in spectral bands beyond the colors visible to humans. Plastic throws off a distinct signature in the near-infrared zone.
- The researchers trained a naive Bayes model on different “spectral signatures” — patterns of light that result when it bounces off plastic, sea water, and debris like driftwood, foam, and seaweed.
- They validated the model using satellite imagery from offshore regions where various types of debris was known to accumulate, including imagery from a previous experiment in which researchers dumped flotillas of plastic off the coast of Greece.
Results: The team tested the model on imagery of coastal sites in western Canada, Ghana, Vietnam, and Scotland. It averaged 86 percent accuracy.
Behind the news: Marine scientists are finding a variety of uses for AI in ocean conservation. For instance, Google built a neural network that recognizes humpback whale songs using data from the U.S. National Oceanic and Atmospheric Administration. Researchers use the model to follow migrations.
Why it matters: Fish and whales often die from ingesting or getting tangled in pieces of plastic. As the material breaks down into tiny fragments, it gets eaten by smaller organisms, which get eaten by larger organisms, including fish consumed by humans, with potentially toxic effects.
We’re thinking: Pointing this model at the beach might be even more helpful: Most ocean plastic originates on land, so coastlines may be the best places to capture it before it enters the food web.