AutoTerm: an automated pipeline for glacier terminus extraction using machine learning and a “big data” repository of Greenland glacier termini

Zhang, Enze; Catania, Ginny; Trugman, Daniel T.

doi:https://doi.org/10.5194/tc-17-3485-2023

Articles | Volume 17, issue 8

https://doi.org/10.5194/tc-17-3485-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/tc-17-3485-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 17, issue 8

Research article

|

24 Aug 2023

Research article |

| 24 Aug 2023

AutoTerm: an automated pipeline for glacier terminus extraction using machine learning and a “big data” repository of Greenland glacier termini

Enze Zhang, Ginny Catania, and Daniel T. Trugman

Supplement

https://doi.org/10.5194/tc-17-3485-2023-supplement

Data sets

AutoTerm: A "big data" repository of glacier termini delineated using deep learning (Version 4) Enze Zhang https://doi.org/10.5281/zenodo.7782039

Model code and software

AutoTerm: an automated pipeline of glacier termini delineated using deep learning Enze Zhang https://doi.org/10.5281/zenodo.8270875

Short summary

Glacier termini are essential for studying why glaciers retreat, but they need to be mapped automatically due to the volume of satellite images. Existing automated mapping methods have been limited due to limited automation, lack of quality control, and inadequacy in highly diverse terminus environments. We design a fully automated, deep-learning-based method to produce termini with quality control. We produced 278 239 termini in Greenland and provided a way to deliver new termini regularly.