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Identifying recurrent and persistent landslides using satellite imagery and deep learning: A 30-year analysis of the Himalaya

Lookup NU author(s): Dr Mark KinceyORCiD

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Licence

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).

Publication metadata

Author(s): Chen TK, Kincey ME, Rosser NJ, Seto KC

Publication type: Article

Publication status: Published

Journal: Science of the Total Environment

Year: 2024

Volume: 922

Print publication date: 28/02/2024

Online publication date: 21/02/2024

Acceptance date: 19/02/2024

Date deposited: 15/03/2024

ISSN (print): 0048-9697

ISSN (electronic): 1879-1026

Publisher: Elsevier BV

URL: https://doi.org/10.1016/j.scitotenv.2024.171161

DOI: 10.1016/j.scitotenv.2024.171161

Data Access Statement: The satellite images, training data, and multi-temporal landslide inventory, and recurrence, persistence, and first occurrence maps are available on GitHub (https://github.com/karenthchen/Persistent-recurrent-landslide-Himalaya).

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Funding

Funder referenceFunder name
201844-112
Gaylord Donnelley Environmental Postdoctoral Fellowship
Global Challenges Research Fund Multi-Hazard and Systemic Risk programme
LCLUC
NASA
NE/T01038X/1
NNX17AH98G
UKRI-DFID SHEAR program

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