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DARTS: Multi-year database of AI-detected retrogressive thaw slumps in the circum-arctic permafrost region

DARTS: Multi-year database of AI-detected retrogressive thaw slumps in the circum-arctic permafrost region

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1038/s41597-025-05810-2. This is version 2 of this Preprint.

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Authors

Ingmar Nitze , Konrad Heidler, Nina Nesterova, Jonas Küpper, Emma Schütt, Tobias Hölzer, Sophia Barth, Mark Lara, Anna K Liljedahl, Guido Grosse

Abstract

Retrogressive Thaw Slumps (RTS) are widespread mass-wasting hillslope failures triggered by thawing permafrost. While regional studies have provided insights into the spatial distribution and dynamics of RTS, a consistent and unbiased quantification and monitoring remains unsolved at pan-arctic scales. We present the Database of AI-detected Arctic RTS footprints (DARTS), comprising ~43,000 individual footprints of active RTS or active areas within larger RTS landforms. DARTS spans ~1.6 million km2 from 2018–2023, with at least annual coverage from 2021–2023 across a ~900,000 km2 region. The database is freely available in two processing levels: sub-annual and annually aggregated polygon footprints including spatial and tabular metadata. DARTS uses a highly automated workflow based on deep learning segmentation of PlanetScope multi-spectral satellite imagery (3–5 m resolution) and elevation data. Validation against different regional RTS datasets yielded F1 scores ranging from 0.263 to 0.700, with higher accuracy in areas of intense RTS activity. DARTS provides a valuable resource for systematically mapping, quantifying, and analyzing active hillslope thermokarst distribution and changes over time across the circum-arctic permafrost region.

DOI

https://doi.org/10.31223/X5740Z

Subjects

Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Geomorphology, Physical Sciences and Mathematics

Keywords

Permafrost, retrogressive thaw slumps, Dataset, pan-arctic, Deep learning, Artificial Intelligence, thermokarst, active layer detachment slides, hillslope thermokarst, remote sensing, Segmentation

Dates

Published: 2024-10-20 15:21

Last Updated: 2025-09-01 20:55

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License

CC BY Attribution 4.0 International

Additional Metadata

Conflict of interest statement:
None