AI Could Increase Remote Monitoring of Glacier Melting

Tracking how fast glaciers are shrinking is crucial for measuring the pace of climate change and projecting future sea level rises. This is normally a painstaking manual job, but a new approach that enables AI to analyze satellite images of glaciers anywhere in the world could help automate the monitoring process.

Glaciers that flow directly into the ocean play a crucial role in the earth’s climate, but global warming is making them retreat ever faster. This can have severe knock-on effects as ice that breaks away from “calving fronts”—the ends of glaciers where icebergs shear off into the water—dumps massive amounts of freshwater into the sea, which can alter ocean currents and cause sea levels to rise. Bright white glaciers also reflect a lot of sunlight. When they shrink, they expose dark seawater that absorbs heat from the sun.

All of this means that tracking glacier loss is critical for understanding how both local and global climate conditions will change over time. But the number of glaciers that need to be monitored around the world far outstrips the capacity of human analysts. There is hope that AI-based image analysis could help plug the gap, but previous models have performed poorly on regions not included in their training data. This severely limits the applicability of the approach, given how difficult it is to collect manually-labeled images.

Now, a paper accepted to the IEEE International Conference on Image Processing (ICIP) shows that a leading deep learning model for tracing glacier calving fronts can be adapted to new locations with minimal additional data. Researchers from the Friedrich-Alexander University of Erlangen–Nuremberg (FAU), in Germany, showed that the model’s error—the average distance between the modeled boundary and the real one—was cut from more than a kilometer to less than 70 meters by providing three pieces of information: one hand-labeled image per glacier, unlabeled summer reference images, and a map of the underlying rock.

In related research, some of the paper’s authors have already put the approach to work, using it to extract monthly calving front positions for all 145 glaciers in Norway’s Svalbard archipelago from 2015 to 2024. The team now hopes to extend the approach to another 1,500 glaciers in the Arctic.

“It’s about understanding glaciers better and how they react to changes in the climate,” says Nora Gourmelon, a Ph.D. student at FAU and co-lead author of the ICIP paper. “When you know about the past, then you will also hopefully be better able to understand how they will change in the future.”

Reducing the margin of error

Historically, delineating calving fronts has required students and researchers to pour over satellite radar images to manually trace the boundary between glaciers and the ocean, says Gourmelon. The process is time-consuming though, so numerous research groups have been experimenting with using computer vision models to automate the process.

In 2023,…