New AI system can map giant icebergs from satellite images 10,000 times faster than humans
Scientists 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 ...
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.
Icebergs have a significant impact on the polar environment and monitoring them is critical for both maritime safety and scientific study. They can be extremely large—in some cases the size of small countries—and can pose a risk to passing ships. As they melt, icebergs release nutrients and freshwater into the seas, and this can have an impact on marine ecosystems.
Icebergs have a significant impact on the polar environment and monitoring them is critical for both maritime safety and scientific study. They can be extremely large—in some cases the size of small countries—and can pose a risk to passing ships. As they melt, icebergs release nutrients and freshwater into the seas, and this can have an impact on marine ecosystems.
Dr. Anne Braakmann-Folgmann, who led the study while undertaking doctoral research at the Centre for Polar Observation and Monitoring at the University of Leeds, said, "Icebergs exist in hard-to-reach parts of the world and satellites are not only a fantastic tool to observe where they are, they can help scientists understand the process of how they melt and eventually begin to break apart.
"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."
"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."
Image 1 shows the U-net algorithm correctly identifying the iceberg, which is highlighted in red. In comparison, the k-means algorithm has incorrectly identified a cluster of smaller icebergs and ice fragments, shown in blue, as one large iceberg. That is revealed in image 2. Credit: Dr Anne Braakmann-Folgmann and the European Space Agency.
Image 1 shows the U-net algorithm correctly identifying the iceberg, which is highlighted in red. In comparison, the k-means algorithm has incorrectly identified a cluster of smaller icebergs and ice fragments, shown in blue, as one large iceberg. That is revealed in image 2. Credit: Dr Anne Braakmann-Folgmann and the European Space Agency.
Image 3 shows the U-net correctly identifying the iceberg, this time surrounded by sea ice. The iceberg is highlighted in red, and the sea ice is seen as a grey structure. However, the k-means algorithm has identified the iceberg and the sea ice as a single iceberg. It is unable to differentiate between the two, despite them being distinct objects, where sea ice is rather flat ice on the sea and an iceberg standing meters above it. That is shown in Image 4. Credit: Dr Anne Braakmann-Folgmann and the European Space Agency
Image 3 shows the U-net correctly identifying the iceberg, this time surrounded by sea ice. The iceberg is highlighted in red, and the sea ice is seen as a grey structure. However, the k-means algorithm has identified the iceberg and the sea ice as a single iceberg. It is unable to differentiate between the two, despite them being distinct objects, where sea ice is rather flat ice on the sea and an iceberg standing meters above it. That is shown in Image 4. Credit: Dr Anne Braakmann-Folgmann and the European Space Agency