Using Deep Learning for Glacier Thickness Estimation at a Regional Scale-Reference-Cited by-同舟云学术

Using Deep Learning for Glacier Thickness Estimation at a Regional Scale

Published:2024 Issue: Volume:21 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Lopez Uroz Lorenzo¹^ORCID,Yan Yajing¹^ORCID,Benoit Alexandre¹^ORCID,Rabatel Antoine²^ORCID,Giffard-Roisin Sophie³,Lin-Kwong-Chon Christophe¹

Affiliation:

1. Laboratoire d’Informatique, Systèmes, Traitement de l’Information et de la Connaissance (LISTIC), University Savoie Mont Blanc, Annecy-le-Vieux, France

2. Institut des Géosciences de l’Environnement (IGE), University Grenoble Alpes, CNRS, IRD, INRAE, Grenoble-INP, Grenoble, France

3. Institut des Sciences de la Terre (ISTerre), IRD, University Grenoble Alpes, Grenoble, France

Funder

Agence Nationale de la Recherche, project Recursive Estimation and Prediction of Earth Deformation from SAR Image Time Series

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Geotechnical Engineering and Engineering Geology

Link

http://xplorestaging.ieee.org/ielx7/8859/10365397/10400574.pdf?arnumber=10400574

Reference19 articles.

1. A consensus estimate for the ice thickness distribution of all glaciers on Earth

2. Ice thickness distribution of all Swiss glaciers based on extended ground-penetrating radar data and glaciological modeling

3. How accurate are estimates of glacier ice thickness? Results from ITMIX, the Ice Thickness Models Intercomparison eXperiment

4. Estimation of Glacier Thickness From Surface Mass Balance and Ice Flow Velocities: A Case Study on Argentière Glacier, France

5. Ice velocity and thickness of the world’s glaciers