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Date
2021Type
- Conference Paper
ETH Bibliography
yes
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Abstract
On 2nd and 3rd October 2020, Storm Alex hit northern Italy and southern France regions with 500 mm of rainfall in about 24 hours. This triggered devastating flash floods and landslides, causing severe damages and 15 fatalities. This study presents a landslide inventory map obtained by using a generalized deep-learning model, avoiding human interaction in the workflow by skipping the time-consuming training step. A total of 1,249 landslides have been mapped with this approach in minutes after a suitable post-event satellite image was available for processing. Our results show how deep-learning strategies applied to remote sensing data can help in the aftermath of catastrophic events for the rapid detection and mapping of landslide phenomena. Show more
Publication status
publishedExternal links
Book title
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSSPages / Article No.
Publisher
IEEEEvent
Subject
Landslide; Rapid mapping; Convolutional neural network (CNN); Deep-learning; Storm AlexOrganisational unit
03465 - Löw, Simon (emeritus) / Löw, Simon (emeritus)
Related publications and datasets
Is part of: http://hdl.handle.net/20.500.11850/526911
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ETH Bibliography
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