Pixel-based classification method for earthquake-induced landslide mapping using remotely sensed imagery, geospatial data and temporal change information-Reference-Cited by-同舟云学术

Pixel-based classification method for earthquake-induced landslide mapping using remotely sensed imagery, geospatial data and temporal change information

Published:2024-02-04 Issue:6 Volume:120 Page:5163-5200
ISSN:0921-030X
Container-title:Natural Hazards
language:en
Short-container-title:Nat Hazards

Author:

Asadi Adel^ORCID,Baise Laurie G.,Koch Magaly,Moaveni Babak,Chatterjee Snehamoy,Aimaiti Yusupujiang

Funder

U.S. Geological Survey

National Geospatial-Intelligence Agency

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11069-023-06399-8.pdf

Reference110 articles.

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2. Aimaiti Y, Liu W, Yamazaki F, Maruyama Y (2019) Earthquake-induced landslide mapping for the 2018 Hokkaido Eastern Iburi earthquake using PALSAR-2 data. Remote Sens 11:2351. https://doi.org/10.3390/rs11202351

3. Allstadt KE, Thompson EM, Jibson RW et al (2022) The US geological survey ground failure product: near-real-time estimates of earthquake-triggered landslides and liquefaction. Earthq Spectra 38(1):5–36. https://doi.org/10.1177/87552930211032685

4. Asadi A, Baise LG, Sanon C, Koch M, Chatterjee S, Moaveni B (2023) Semi-supervised learning method for the augmentation of an incomplete image-based inventory of earthquake-induced soil liquefaction surface effects. Remote Sens 15(19):4883. https://doi.org/10.3390/rs15194883

5. Bai Y, Adriano B, Mas E, Koshimura S (2017) Machine learning based building damage mapping from the ALOS-2/PALSAR-2 SAR imagery: case study of 2016 Kumamoto earthquake. J Disaster Res 12:646–655

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