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An Artificial Neural Network Joins the Fight Against Receding Glaciers

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Satellite image of the calving front of a crevassed glacier in southwest Greenland.

A new neural network known as the Calving Front Machine is capable of automatically identifying the calving fronts of ocean-terminating glaciers from decades of satellite imagery.

Credit: Christy Hansen/NASA

An artificial neural network developed by University of California, Irvine (UCI) researchers can autonomously recognize and quantify the edges of glaciers in satellite images with far greater reliability than humans.

The team trained the Calving Front Machine (CALFIN) network on tens of thousands of images; afterwards, CALFIN could measure calving fronts to within an average of 82 meters (269 feet) from their true locations, outperforming previous models.

Said William Colgan of the Geological Survey of Denmark and Greenland, “I think machine learning now offers a robust way of upscaling a handful of site-specific and process-level observations to tell a bigger regional story."

From Columbia Climate School, Columbia University
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Abstracts Copyright © 2021 SmithBucklin, Washington, DC, USA


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