UMBC's Maryam Rahnemoonfar Eyes Arctic Ice and Snow Data Through AI
January 25, 2021
UMBC researchers have developed a technique to more quickly analyze
extensive data from Arctic ice sheets to gain insight and useful
knowledge on patterns and trends.
Over the years, vast amounts of data have been collected about the
Arctic and Antarctic ice. These data are essential for scientists
and policymakers seeking to understand climate change and the
current trend of melting. Masoud Yari, research assistant professor,
and Maryam Rahnemoonfar, associate professor of information systems,
have utilized new AI technology to develop a fully automatic
technique to analyze ice data.
This public impact research is part of the National Science
Foundation’s ongoing BigData project. Rahnemoonfar, Yari, and
colleagues have published their findings in the Journal of
Rethinking manual techniques
For decades, researchers have kept close track of polar ice, snow,
and soil measurements, but processing the large volume of available
data has proven challenging. NASA’s processes for collecting,
tracking, and labeling polar data involve significant manual work,
and changes detected in the data can take months or even years to
see. Even Arctic data collected via remote sensing technologies
require manual processing.
According to Rahnemoonfar, “Radar big data is very difficult to mine
and understand just by using manual techniques.”
The AI techniques the researchers are developing can be used to mine
the data more quickly. They help scientists get useful information
on trends related to the thickness of the ice sheets and the level
of snow accumulation in a certain location.
researchers developed an algorithm that learns how to identify
objects and patterns within the Arctic and Antarctic data. An AI
algorithm must be exposed to hundreds of thousands of examples to
learn how to identify important elements and patterns. Rahnemoonfar
and her team used existing incomplete and noisy labeled data from
the Arctic to train the AI algorithm on how to categorize and
understand new data.
The algorithm’s training is not yet complete. Researchers will need
to scale it up over multiple sensors and locations to create a more
accurate tool. However, it has already successfully begun to
automate a process that was previously inefficient and
The rapid expansion of using AI to understand ice and snow thickness
in the Arctic will allow scientists and researchers to make faster
and more accurate predictions to inform dialogue about climate
change. The rate at which Arctic ice is melting impacts sea levels.
If scientists are better able to predict the severity of the
melting, society can better mitigate the harm caused by sea level