New AI System Can Map Giant Icebergs From Satellite Images 10,000 Times Faster Than Humanshttps://phys.org/news/2023-11-ai-giant-icebergs-satellite-images.htmlScientists have trained an artificial intelligence (AI) system to accurately map—in one-hundredth of a second—the surface area and outline of giant icebergs captured on satellite images. The paper, titled
"Mapping the extent of giant Antarctic icebergs with Deep Learning," is published in
The Cryosphere.It is a major advance on existing automated systems, which struggle to distinguish icebergs from other features in the image. Manual—or human—interpretation of the image is more accurate, but it can take several minutes to delineate the outline of a single iceberg. If that has to be repeated numerous times, the process quickly becomes time-consuming and laborious.
"Using the new AI system overcomes some of the problems with existing automated approaches, which can struggle to distinguish between icebergs and other ice floating on the sea or even a nearby coastline which are present in the same image."
Dr. Braakmann-Folgmann and her colleagues used an algorithm called U-net—a type of neural network—to "train" a computer to accurately map the outline of icebergs from images taken by Sentinel-1 satellites operated by the European Space Agency.
As part of the study, the effectiveness of the U-net algorithm was compared to two other state-of-the-art algorithms used to map icebergs. They are known as k-means and Otsu. The algorithms were programmed to identify the biggest iceberg in a series of satellite images.
The system has been tested on satellite images of seven icebergs, which were all between the size of the city of Bern—54 km2; and Hong Kong—1,052 km2. For each of these icebergs, up to 46 images were used that covered all seasons from 2014-2020.
Over a series of tests, U-net outperformed the other two algorithms and was more effective in delineating the outline of an iceberg in images taken when environmental conditions were challenging, such as the image capturing a lot of ice structures.
On average, the U-net algorithm showed only a 5% lower estimate of the area of an iceberg. In contrast, the k-means and Otsu algorithms returned—on average—figures for iceberg area that were 150% to 170% too large, probably because the algorithms were including sea ice and even nearby coastline in the calculations.
In machine learning, the F1 score is an evaluation of how well an algorithm performs and ranges from 0 to 1, with values closer to one displaying more precision. U-net achieved an F1 score of 0.84. The two other algorithms both scored 0.62.
Mapping the extent of giant Antarctic icebergs with Deep Learning,
The Cryosphere (2023).
https://tc.copernicus.org/articles/17/4675/2023/
