Using Deep Learning to Advance Global Monitoring of Retrogressive Thaw Slumps at High Spatio-Temporal Resolution
Nitze, Ingmar ORCID: https://orcid.org/0000-0002-1165-6852, Heidler, Konrad, Maier, Kathrin, Barth, Sofia, Nesterova, Nina, Schütt, Emma, Küpper, Jonas, Hölzer, Tobias, Liljedahl, Anna and Grosse, Guido
;
Contact
ingmar.nitze [ at ] awi.de
Abstract
Item Type
Conference
(Lecture)
Authors
Nitze, Ingmar ORCID: https://orcid.org/0000-0002-1165-6852, Heidler, Konrad, Maier, Kathrin, Barth, Sofia, Nesterova, Nina, Schütt, Emma, Küpper, Jonas, Hölzer, Tobias, Liljedahl, Anna and Grosse, Guido
;
Divisions
AWI Organizations > Geosciences > Permafrost Research
AWI Organizations > Infrastructure > Computing and Data Centre
AWI Organizations > Infrastructure > Computing and Data Centre
Primary Division
Programs
Helmholtz Research Programs > CHANGING EARTH (2021-2027) > PT5:Dynamics of the Terrestrial Environment and Freshwater Resources under Global and Climate Change > ST5.3: Natural dynamics of the terrestrial Earth surface system
Primary Topic
Helmholtz Programs > Helmholtz Research Programs > CHANGING EARTH (2021-2027) > PT5:Dynamics of the Terrestrial Environment and Freshwater Resources under Global and Climate Change
Publication Status
Published
Eprint ID
58878
Cite as
Nitze, I.
,
Heidler, K.
,
Maier, K.
,
Barth, S.
,
Nesterova, N.
,
Schütt, E.
,
Küpper, J.
,
Hölzer, T.
,
Liljedahl, A.
and
Grosse, G.
(2024):
Using Deep Learning to Advance Global Monitoring of Retrogressive Thaw Slumps at High Spatio-Temporal Resolution
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