Detecting Earthquake-triggered Large-scale Landslides with Different Input Window Sizes Convolutional Neural Networks

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragForschungBegutachtung

Abstract

Earthquake‐triggered large‐scale landslides are considered one of the most destructive natural hazards to human lives and infrastructure in many mountain ranges of the world (Hölbling et al. 2012). Information about the exact location of landslides is important for post‐disaster humanitarian response. Although some field surveying approaches are available, the remoteness of mountainous areas makes it often hard or even impossible to reach the affected area (Prasicek et al. 2018). Therefore, the differential synthetic aperture radar interferometry (DInSAR) and Earth observation (EO) data are widely considered as the most accessible data providing up‐to‐date information needed to support planning and crisis responses.

OriginalspracheEnglisch
TitelGeophysical Research Abstracts Vol. 21, EGU General Assembly 2019-4477
ErscheinungsortVienna, Austria
PublikationsstatusVeröffentlicht - 1 Apr 2019
VeranstaltungEGU General Assembly 2019 - Wien, Wien, Österreich
Dauer: 7 Apr 201912 Apr 2019
http://egu2019.eu

Konferenz

KonferenzEGU General Assembly 2019
KurztitelEGU 2019
LandÖsterreich
OrtWien
Zeitraum7/04/1912/04/19
Internetadresse

Bibliographische Notiz

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Systematik der Wissenschaftszweige 2012

  • 105 Geowissenschaften

Zitieren

Ghorbanzadeh, O., Hölbling, D. W., Raj Meena, S., & Blaschke, T. (2019). Detecting Earthquake-triggered Large-scale Landslides with Different Input Window Sizes Convolutional Neural Networks. in Geophysical Research Abstracts Vol. 21, EGU General Assembly 2019-4477 Vienna, Austria.