AI Can Help Track the World's Shrinking Glaciers
Researchers at Friedrich-Alexander University of Erlangen-Nuremberg (FAU) in Germany have shown that a deep learning model can track the retreating edges of glaciers almost anywhere on Earth after seeing only a single hand-labeled image per glacier. By adding summer reference photos and a map of the underlying rock, the team cut the model's average error from more than a kilometer to under 70 meters. The work, accepted to the IEEE International Conference on Image Processing (ICIP), points toward automated, large-scale glacier monitoring at a time when warming is speeding up ice loss.
Key Takeaways
- 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.
- 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.
- 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 process is time-consuming though, so numerous research groups have been experimenting with using computer vision models to automate the process.
In 2023, Gourmelon and her colleagues produced a dataset of 681 radar images of seven glaciers in Antarctica, Greenland, and Alaska, with manually annotated calving fronts to help train and benchmark new models.
- They then developed two novel strategies to further improve accuracy.
Stats & Key Facts
- #By adding summer reference photos and a map of the underlying rock, the team cut the model's average error from more than a kilometer to under 70 meters.
- #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 2023, Gourmelon and her colleagues produced a dataset of 681 radar images of seven glaciers in Antarctica, Greenland, and Alaska, with manually annotated calving fronts to help train and benchmark new models.
- #But when they took a state-of-the-art deep learning model trained on this dataset and applied it to previously unseen glaciers in Svalbard, they found it was off by an average of 1,131.6 m.
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.
For more details please read the original article at IEEE Spectrum AI.
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