A collaborative and scalable geospatial data set for arctic retrogressive thaw slumps with data standards

Yili Yang*, Heidi Rodenhizer*, Brendan M. Rogers*, Jacqueline Dean, Ridhima Singh, Tiffany Windholz, Amanda Poston, Stefano Potter, Scott Zolkos, Greg Fiske, Jennifer Watts, Lingcao Huang, Chandi Witharana, Ingmar Nitze, Nina Nesterova, Sophia Barth, Guido Grosse, Trevor Lantz, Alexandra Runge, Luigi LombardoIonut Cristi Nicu, Lena Rubensdotter, Eirini Makopoulou, Susan Natali

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
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Abstract

Arctic permafrost is undergoing rapid changes due to climate warming in high latitudes. Retrogressive thaw slumps (RTS) are one of the most abrupt and impactful thermal-denudation events that change Arctic landscapes and accelerate carbon feedbacks. Their spatial distribution remains poorly characterised due to time-intensive conventional mapping methods. While numerous RTS studies have published standalone digitisation datasets, the lack of a centralised, unified database has limited their utilisation, affecting the scale of RTS studies and the generalisation ability of deep learning models. To address this, we established the Arctic Retrogressive Thaw Slumps (ARTS) dataset containing 23,529 RTS-present and 20,434 RTS-absent digitisations from 20 standalone datasets. We also proposed a Data Curation Framework as a working standard for RTS digitisations. This dataset is designed to be comprehensive, accessible, contributable, and adaptable for various RTS-related studies. This dataset and its accompanying curation framework establish a foundation for enhanced collaboration in RTS research, facilitating standardised data sharing and comprehensive analyses across the Arctic permafrost research community.

Original languageEnglish
Article number18
JournalScientific Data
Volume12
Issue number1
DOIs
Publication statusPublished - 6 Jan 2025

Keywords

  • ITC-GOLD
  • ITC-ISI-JOURNAL-ARTICLE

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