Landslide detection from bitemporal satellite imagery using attention-based deep neural networks-Reference-Cited by-同舟云学术

Landslide detection from bitemporal satellite imagery using attention-based deep neural networks

Published:2022-06-17 Issue:10 Volume:19 Page:2459-2471
ISSN:1612-510X
Container-title:Landslides
language:en
Short-container-title:Landslides

Author:

Amankwah Solomon Obiri Yeboah^ORCID,Wang Guojie,Gnyawali Kaushal,Hagan Daniel Fiifi Tawiah,Sarfo Isaac,Zhen Dong,Nooni Isaac Kwesi,Ullah Waheed,Duan Zheng

Publisher

Springer Science and Business Media LLC

Subject

Geotechnical Engineering and Engineering Geology

Link

https://link.springer.com/content/pdf/10.1007/s10346-022-01915-6.pdf

Reference48 articles.

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2. Bacha AS, Shafique M, van der Werff H (2018) Landslide inventory and susceptibility modelling using geospatial tools, in Hunza-Nagar valley, northern Pakistan. J Mt Sci 15:1354–1370. https://doi.org/10.1007/s11629-017-4697-0

3. Bhushal R (2020) Why are landslides so deadly in Nepal? In: Third Pole. https://www.thethirdpole.net/en/climate/why-are-landslides-sodeadly-in-nepal/. Accessed 11 Dec 2021

4. Chen H, Shi Z (2020) A spatial-temporal attention-based method and a new dataset for remote sensing image change detection. Remote Sens 12. https://doi.org/10.3390/rs12101662

5. Chen H, Qi Z, Shi Z (2022) Remote sensing image change detection With transformers. IEEE Trans Geosci Remote Sens 60:1–14. https://doi.org/10.1109/TGRS.2021.3095166

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