Interpretation of Structures in Polar Regions with Deep Learning Methods

Heidler, Konrad Marten Harald

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Meßtechnik, Steuerungs- und Regelungstechnik, Automation

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Original title:: Interpretation of Structures in Polar Regions with Deep Learning Methods
Translated title:: Interpretation von Strukturen in Polarregionen mithilfe von Deep Learning Methoden
Author:: Heidler, Konrad Marten Harald
Year:: 2024
Document type:: Dissertation
Faculty/School:: TUM School of Engineering and Design
Institution:: Data Science in Earth Observation (Prof. Zhu)
Advisor:: Zhu, Xiaoxiang (Prof. Dr. habil.)
Referee:: Zhu, Xiaoxiang (Prof. Dr. habil.); Bamler, Richard Hans Georg (Prof. Dr. habil.); Lefèvre, Sébastien (Prof. Dr.)
Language:: en
Subject group:: GEO Geowissenschaften
TUM classification:: BAU 967; MSR 915
Abstract:: Global climate change is rapidly changing the polar regions. In an effort to support monitoring these changes, this thesis develops deep learning methods for the remote sensing analysis of targets in these regions. Firstly, models for mapping glacier calving fronts are developed by rethinking how the task is encoded computationally. Secondly, the feasibility of detecting retrogressive thaw slumps in permafrost areas is established and made data-efficient through semi-supervised learning.
Translated abstract:: Der globale Klimawandel hat massive Auswirkungen auf die Polarregionen. Diese Dissertation entwickelt Deep Learning Methoden für die Fernerkundung bestimmter Objekte in diesen Regionen. Zuerst werden Modelle für die Kartierung von Gletscherkalbungsfronten entwickelt, indem neue Ansätze für deren Kodierung verfolgt werden. Weiterhin werden Modelle für die Detektion von Retrogressiven Taurutschungen in Permafrostregionen mithilfe von Trainingstechniken wie Semi-Supervised Learning optimiert.
WWW:: https://mediatum.ub.tum.de/?id=1731924
Date of submission:: 22.01.2024
Oral examination:: 06.06.2024
File size:: 73620508 bytes
Pages:: 177
Urn (citeable URL):: https://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:91-diss-20240606-1731924-1-2
Last change:: 12.07.2024
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