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

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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.

Original languageEnglish
Title of host publicationGeophysical Research Abstracts Vol. 21, EGU General Assembly 2019-4477
Place of PublicationVienna, Austria
Publication statusPublished - 1 Apr 2019
EventEGU General Assembly 2019 - Wien, Wien, Austria
Duration: 7 Apr 201912 Apr 2019
http://egu2019.eu

Conference

ConferenceEGU General Assembly 2019
Abbreviated titleEGU 2019
CountryAustria
CityWien
Period7/04/1912/04/19
Internet address

Bibliographical note

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Fields of Science and Technology Classification 2012

  • 105 Geosciences

Keywords

  • Convolutional Neural Networks;
  • Input Window Size;
  • Earthquake-triggered Large-scale Landslides

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