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Using Hyperspectral Machine Learning Models to Combat Climate Change

Researchers from the University of Oxford have created a tool that uses machine learning and hyperspectral data from Trillium Technologies’ NIO.space to autonomously detect methane plumes on Earth from orbit. This could make it easier to pinpoint methane “super emitters” and allow for more efficient greenhouse gas emission reduction. The journal Nature Scientific Reports has published the findings.
United States Megan Craig Andrei Armiagov Shutterstock Professor Andrew Markham Department Of Computer Science University Of Oxford European Space Agency

Researchers successfully train a machine lear

• Researchers used an innovative machine learning approach to develop a tiny model capable of running on a satellite’s limited processing power; • The trained model successfully detected cloud cover in satellite images in around a tenth of a second; • The model could easily be adapted to enable automated decision making for a range of purposes, from disaster management to deforestation.
United Kingdom Czech Republic Andrew Markham Caroline Wood International Geoscience Times Higher Education World University Rankings

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