Tracking permafrost landscape dynamics in a rapidly warming Arctic
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The Arctic is warming at an unprecedented pace, which leads to rapid landscape changes, particularly in the terrestrial Arctic which is largely underlain by permafrost. These landscape dynamics have strong impacts on local (geohazards, infrastructure) to global (climate system) scales. The integration of Machine Learning (ML) and Artificial Intelligence (AI) techniques with remote sensing data has become the standard approach for mapping these intricate landscape transformations within Arctic permafrost. Yet, what is going on in the Arctic remains still unknown in many places. Here we specifically dive into our analysis tracking Retrogressive Thaw Slumps (RTS), which are typical landscape processes of thawing and degrading permafrost. We will show the advances and challenges of using Deep-Learning techniques for tracking these comparably small, but dynamic landscape features across the Arctic. Finally, we will dig into further AI based applications in permafrost science, tracking changes in geomorphological features, but also tracking wildlife related to the massive changes in the northern permafrost region.