Deep learning reveals one of Earth's largest landslide terrain in Patagonia

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17310%2F22%3AN2302J1Z" target="_blank" >RIV/61988987:17310/22:N2302J1Z - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0012821X22002783?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0012821X22002783?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.epsl.2022.117642" target="_blank" >10.1016/j.epsl.2022.117642</a>

Result language

angličtina

Original language name

Deep learning reveals one of Earth's largest landslide terrain in Patagonia

Original language description

Hundreds of basaltic plateau margins east of the Patagonian Cordillera are undermined by numerous giant slope failures. However, the overall extent of this widespread type of plateau collapse remains unknown and incompletely captured in local maps. To detect giant slope failures consistently throughout the region, we train two convolutional neural networks (CNNs), AlexNet and U-Net, with Sentinel-2 optical data and TanDEM-X topographic data on elevation, surface roughness, and curvature. We validated the performance of these CNNs with independent testing data and found that AlexNet performed better when learned on topographic data, and UNet when learned on optical data. AlexNet predicts a total landslide area of 12,000 km2 in a study area of 450,000 km2, and thus one of Earth's largest clusters of giant landslides. These are mostly lateral spreads and rotational failures in effusive rocks, particularly eroding the margins of basaltic plateaus; some giant landslides occurred along shores of former glacial lakes, but are least prevalent in Quaternary sedimentary rocks. Given the roughly comparable topographic, climatic, and seismic conditions in our study area, we infer that basalts topping weak sedimentary rocks may have elevated potential for large-scale slope failure. Judging from the many newly detected and previously unknown landslides, we conclude that CNNs can be a valuable tool to detect large-scale slope instability at the regional scale. However, visual inspection is still necessary to validate results and correctly outline individual landslide source and deposit areas.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10508 - Physical geography

Project

<a href="/en/project/GA19-16013S" target="_blank" >GA19-16013S: Giant landslides in glacier foreland: missing story in the evolution of Patagonian Ice Sheet and related glacial lakes</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Earth and Planetary Science Letters

ISSN

0012821X

e-ISSN

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Volume of the periodical

—

Issue of the periodical within the volume

September

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

13

Pages from-to

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UT code for WoS article

000812165000002

EID of the result in the Scopus database

2-s2.0-85131463867