Speaker
Description
We present our work on the latest version of AWIs Database of AI-detected Retrogressive Thaw Slumps (DARTS) using a pan-arctic coverage from Sentinel-2 Quarterly Mosaics (~20 Mio km² annually) and auxiliary data sources. Retrogressive Thaw Slumps (RTS) are abrupt disturbances in ice-rich permafrost that dramatically change topography, hydrology, biogeochemistry, and local ecosystems by permafrost thaw, erosion, and massive sediment mobilization. These features increase in abundance and size in many Arctic regions and a spatio-temporal understanding of their dynamics is crucial for assessing impacts of climate change and disturbances on permafrost ecosystems.
We cover lessons learned during the creation of the DARTS dataset – building on large amounts of Earth Observation data – and the relevant processing pipelines, as well as the implementation in a Deep Learning inference workflow. In this presentation, we provide our insights into data acquisition, usage of geospatial Python libraries as well as the efficient and performant processing and storage on different systems/infrastructure. Data processing required transfer and storage of multiple terabytes of data and used AWIs High Performance Compute cluster Albedo as well as compute resources provided through HAICORE (JUWELS Booster at the Jülich Supercomputing Center). Efficient provisioning data for Artificial Intelligence processing included utilization of modern file formats for vector (GeoParquet) and multidimensional raster (ZARR/Icechunk) data.
We also highlight how Geospatial Data Science in polar regions faces numerous challenges specific to the data formatting and georeferencing. This is particularly true for the acquisition and processing of data with circum-polar coverage in mind, concerning choice of appropriate georeference systems due to coordinate wrapping and raster distortion.