Researchers speed up analysis of Arctic ice and snow data through artificial intelligence

Researchers at the University of Maryland, Baltimore County have developed a technique for quicker analysis of extensive data from Arctic ice sheets to gain knowledge of patterns and trends.

Over the years, vast amounts of data have been collected about Arctic and Antarctic ice. These data are essential for scientists and policymakers seeking to understand climate change and the current trend of melting.

Researchers Masoud Yari and Maryam Rahnemoonfar have utilized new AI technology to develop a fully automatic technique to analyze ice data. They describe the technology in the Journal of Glaciology. Their effort is part of the U.S. National Science Foundation’s ongoing BigData project. The data build on new image-processing algorithms developed by John Paden at the University of Kansas.

“It is great to see the cooperation between computer vision and machine learning to help predict ice changes,” said Sylvia Spengler, a program director in NSF’s Computer and Information Science and Engineering Directorate.

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