Artificial Intelligence for Cold Regions (AI-CORE) - a Pilot to bridge Data Analytics and Infrastructure Development

• Main Authors: Nitze, Ingmar, Phan, Long Duc, Christmann, Julia, Rueckamp, Martin, Humbert, Angelika, Grosse, Guido, Frickenhaus, Stephan, Dinter, Tilman, Heidler, Konrad, Barth, Sophia
• Format: Conference Object
• Language: unknown
• Published: 2021
• Subjects: Greenland; Ice; Ice Sheet; permafrost
• Online Access: https://epic.awi.de/id/eprint/53603/; https://epic.awi.de/id/eprint/53603/1/AI_Core_DSS_final_v2.pdf; https://hdl.handle.net/10013/epic.f74b496c-0aa1-47a8-83ef-222f68297f0e

Artificial Intelligence for Cold Regions (AI-CORE) is a collaborative project of the DLR, the AWI, the TU Dresden, and is funded by the Helmholtz Foundation since early 2020. The project aims at developing AI methods for addressing some of the most challenging research questions in cryosphere remote sensing, rapidly changing ice sheets and thawing permafrost. We apply data analytics approaches to discover the data variable from data set simulated with an ice sheet model, observe the migration, and time Series analysis to predict and contrast this to simulated grounding line position. For the data assimilation in simulations of the Greenland ice sheet, we engage a level set method, that allows to derive a continuous function in time and space from discrete information at satellite acquisition time steps. We use an alpha-shape method to derive a seamless product of the margin at each time step to be used in the level set method driving the simulations. We develop AI algorithms and tools that allow scaling of our analyses to very large regions. Here we focus on the detection of Retrogressive Thaw Slumps (RTS), highly dynamic erosion processes caused by rapid permafrost thaw. We apply deep-learning based object detection on dense time-series of high-resolution (3m) multi-spectral PlanetScope satellite images and auxiliary datasets such as digital elevation models. RTS detection is challenging, as they are difficult to define semantically and spatially and are highly dynamic and embedded in different landscape settings. The results will help to understand, quantify and predict RTS dynamics and their landscape-scale impacts in a rapidly warming Arctic. We upgrade the base IT-infrastructure at AWI by integrating new GPU computing hardware into the on-premise IT-infrastructure to speed up the computing, data storage capabilities, and parallel processing, supporting the analytical workflows specifically.