Retrieving freeze-thaw states using deep learning with remote sensing data in permafrost landscapes

Author:

Chen YueliORCID,Li Shile,Wang Lingxiao,Mittermeier MagdalenaORCID,Bernier Monique,Ludwig Ralf

Funder

Bundesministerium für Bildung und Forschung

China Scholarship Council

LMU

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Computers in Earth Sciences,Earth-Surface Processes,Global and Planetary Change

Reference62 articles.

1. Agarap, A.F., 2018. Deep learning using rectified linear units (relu). arXiv preprint arXiv:1803.08375. https://doi.org/10.48550/arXiv.1803.08375.

2. European Space Agency. Sentinel-1 GRD. 2021. Accessed June 14, 2022. https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar.

3. The Holocene evolution of permafrost near the tree line, on the eastern coast of Hudson Bay (northern Quebec);Allard;Can. J. Earth Sci.,1987

4. L’impact du Rechauffement Climatique Sur Les Aeroports du Nunavik: Caracteristiques du Pergelisol et Caracterisation des Processus de Degradation Des Pistes;Allard,2007

5. Permafrost and Climate Change in Nunavik and Nunatsiavut: Importance for Municipal and Transportation Infrastructures;Allard,2012

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