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<p>Cryospheric mass movements (CMMs) are accelerating under rapid Arctic and high-mountain warming, yet understanding and predicting them remains challenging due to sparse observations, complex multi-scale processes, and shifting environmental baselines. This Perspective argues that machine learning should advance CMM science not only through prediction, but through transferable and physics-aware inference. We outline a roadmap centered on benchmark datasets, cross-scale generalization, and transdisciplinary collaboration for scientifically grounded hazard assessment.</p>

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Machine learning for cryospheric mass movements: challenges and pathways

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月27日

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  • Te Pei,
  • Louise Farquharson,
  • Margaret Darrow,
  • Tong Qiu,
  • Chaopeng Shen,
  • Yingli Tian,
  • Helena Bergstedt,
  • Shemin Ge,
  • Nicole Guinn,
  • Savinay Nagendra,
  • Ingmar Nitze,
  • Charlotte Pearson,
  • Alexandra Runge,
  • Louise Vick,
  • Michael E. West,
  • Chandi Witharana,
  • Gabriel J. Wolken,
  • Joseph Young,
  • Lukas Arenson

摘要

Cryospheric mass movements (CMMs) are accelerating under rapid Arctic and high-mountain warming, yet understanding and predicting them remains challenging due to sparse observations, complex multi-scale processes, and shifting environmental baselines. This Perspective argues that machine learning should advance CMM science not only through prediction, but through transferable and physics-aware inference. We outline a roadmap centered on benchmark datasets, cross-scale generalization, and transdisciplinary collaboration for scientifically grounded hazard assessment.