PixelDINO: Semi-Supervised Semantic Segmentation for Detecting Permafrost Disturbances in the Arctic-Reference-Cited by-同舟云学术

PixelDINO: Semi-Supervised Semantic Segmentation for Detecting Permafrost Disturbances in the Arctic

Published:2024 Issue: Volume:62 Page:1-12
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Heidler Konrad¹^ORCID,Nitze Ingmar²^ORCID,Grosse Guido²^ORCID,Zhu Xiao Xiang³^ORCID

Affiliation:

1. Chair of Data Science in Earth Observation (SiPEO), Department of Aerospace and Geodesy, School of Engineering and Design, Technical University of Munich (TUM), Munich, Germany

2. Permafrost Research Section, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Potsdam, Germany

3. Chair of Data Science in Earth Observation (SiPEO), Department of Aerospace and Geodesy, School of Engineering and Design and Munich Center for Machine Learning, Technical University of Munich (TUM), Munich, Germany

Funder

German Federal Ministry for Economic Affairs and Climate Action within the framework of the “National Center of Excellence ML4Earth”

Helmholtz-Gemeinschaft deutscher Forschungszentren (HGF) “Artificial Intelligence for Cold Regions” (AI-CORE) Projects

Permafrost Discovery Gateway

Google.org Impact Challenge PDG Grant

Bundesministerium für Bildung und Forschung (BMBF) Future Lab Artificial Intelligence for Earth Observation

Munich Center for Machine Learning

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

https://ieeexplore.ieee.org/ielam/36/10354519/10643625-aam.pdf

Reference66 articles.

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2. Improving satellite-based monitoring of the polar regions: Identification of research and capacity gaps

3. Permafrost Monitoring from Space

4. Review article: Retrogressive thaw slump theory and terminology;Nesterova;EGUsphere,2024

5. The thermal regime of a retrogressive thaw slump near Mayo, Yukon Territory