Potential of Convolutional Neural Networks for Earthquake‐triggered Mass Movement Detection Using Optical and SAR Data

Omid Ghorbanzadeh; Thomas Blaschke

Potential of Convolutional Neural Networks for Earthquake‐triggered Mass Movement Detection Using Optical and SAR Data

Geoinformatik – Z_GIS

Publikation: Konferenzbeitrag › Poster

Abstract

While Convolutional Neural Networks (CNNs) have reached good accuracies for object recognition in aerial images, only a few studies exist that use deep-learning methods and CNNs for landslide detection. The impact of using different input training data on landslide detection by the CNNs is unclear.

Originalsprache	Englisch
Publikationsstatus	Veröffentlicht - 12 Mai 2019
Veranstaltung	ESA Living Planet Symposium 2019 - Milan, Italien Dauer: 13 Mai 2019 → 17 Mai 2019

Konferenz

Konferenz	ESA Living Planet Symposium 2019
Land	Italien
Ort	Milan
Zeitraum	13/05/19 → 17/05/19

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title = "Potential of Convolutional Neural Networks for Earthquake‐triggered Mass Movement Detection Using Optical and SAR Data",

abstract = "While Convolutional Neural Networks (CNNs) have reached good accuracies for object recognition in aerial images, only a few studies exist that use deep-learning methods and CNNs for landslide detection. The impact of using different input training data on landslide detection by the CNNs is unclear.",

keywords = "Convolutional Neural Networks (CNNs);, SAR Data;, Earthquake",

author = "Omid Ghorbanzadeh and Thomas Blaschke",

note = "-; ESA Living Planet Symposium 2019 ; Conference date: 13-05-2019 Through 17-05-2019",

year = "2019",

month = may,

day = "12",

language = "English",

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TY - CONF

T1 - Potential of Convolutional Neural Networks for Earthquake‐triggered Mass Movement Detection Using Optical and SAR Data

AU - Ghorbanzadeh, Omid

AU - Blaschke, Thomas

N1 - -

PY - 2019/5/12

Y1 - 2019/5/12

N2 - While Convolutional Neural Networks (CNNs) have reached good accuracies for object recognition in aerial images, only a few studies exist that use deep-learning methods and CNNs for landslide detection. The impact of using different input training data on landslide detection by the CNNs is unclear.

AB - While Convolutional Neural Networks (CNNs) have reached good accuracies for object recognition in aerial images, only a few studies exist that use deep-learning methods and CNNs for landslide detection. The impact of using different input training data on landslide detection by the CNNs is unclear.

KW - Convolutional Neural Networks (CNNs);

KW - SAR Data;

KW - Earthquake

M3 - Poster

T2 - ESA Living Planet Symposium 2019

Y2 - 13 May 2019 through 17 May 2019

ER -