Developing Artificial Intelligence methods for addressing major challenges in cryosphere research: The AI-CORE project

• Main Authors: Dietz, Andreas, Heidler, Konrad, Nitze, Ingmar, Dinter, Tilman, Hajnsek, Irena, Baumhoer, Celia, Roesel, Anja, Phan, Long Duc, Grosse, Guido, Zhu, Xiao Xiang, Mou, Lichao, Scheinert, Mirko, Frickenhaus, Stephan, Parrella, Giuseppe, Christmann, Julia, Loebel, Erik, Humbert, Angelika
• Format: Conference Object
• Language: unknown
• Published: AGU 2020
• Subjects: Alfred Wegener Institute; Antarc*; Antarctica; glacier; Greenland; Ice; Ice Shelves; permafrost
• Online Access: https://epic.awi.de/id/eprint/53794/; https://agu.confex.com/agu/fm20/meetingapp.cgi/Paper/689963; https://hdl.handle.net/10013/epic.00e1ad9f-a2d5-4492-92b3-8151dcd24034

“Artificial Intelligence for Cold Regions” (AI-CORE) is a collaborative project of the German Aerospace Center (DLR), the Alfred Wegener Institute (AWI), the Technical University Dresden (TU Dresden), and is funded by the Helmholtz Foundation since early 2020. The project aims at developing artificial intelligence methods for addressing some of the most challenging research questions in remote sensing of the cryosphere. Rapidly changing ice sheets and thawing permafrost are big societal challenges, hence quantifying these changes and understanding the mechanisms are of major importance. Given the vast extent of polar regions and the availability of exponentially increasing satellite remote sensing data, intelligent data analysis is urgently required to exploit the full information in satellite time series. This is where AI-CORE comes into play: Four geoscientific use cases have been defined, including a) change pattern identification of outlet glaciers in Greenland; b) object identification in permafrost areas; c) edge detection of calving fronts of glaciers/ice shelves in Antarctica; d) firn line detection and monitoring: The glacier mass balance indicator. For these four use cases, AI-methods are being developed to allow for an accurate, efficient, and automated extraction of the desired parameters. Once these methods have been successfully developed, they will be implemented in processing infrastructures at AWI, TU Dresden, and DLR, and subsequently made available to other research institutes. The presentation will outline the specific goals and challenges of the four use cases as well as the current state of the developments and preliminary results.