Skip to content

Here are the codes for the "3DUNetGSFormer: A deep learning pipeline for complex wetland mapping using generative adversarial networks and Swin transformer" paper.

License

Notifications You must be signed in to change notification settings

aj1365/3DUNetGSFormer

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

11 Commits
 
 
 
 
 
 

Repository files navigation

3DUNetGSFormer

Here is the codes developed for our paper titled "3DUNetGSFormer: A Deep Learning Pipeline for Complex Wetland Mapping using Generative Adversarial Networks and Swin Transformer" published in Ecological Informatics journal.

model

Twitter

Cite our paper at:

Ali Jamali, Masoud Mahdianpari, Brian Brisco, Dehua Mao, Bahram Salehi, Fariba Mohammadimanesh, 3DUNetGSFormer: A deep learning pipeline for complex wetland mapping using generative adversarial networks and Swin transformer, Ecological Informatics, 2022, 101904, ISSN 1574-9541, https://doi.org/10.1016/j.ecoinf.2022.101904. (https://www.sciencedirect.com/science/article/pii/S1574954122003545)

Ali Jamali, Masoud Mahdianpari, Brian Brisco, Dehua Mao, Bahram Salehi, Fariba Mohammadimanesh, 3DUNetGSFormer: A deep learning pipeline for complex wetland mapping using generative adversarial networks and Swin transformer, Ecological Informatics, Volume 72, 2022, 101904, ISSN 1574-9541, https://doi.org/10.1016/j.ecoinf.2022.101904.

About

Here are the codes for the "3DUNetGSFormer: A deep learning pipeline for complex wetland mapping using generative adversarial networks and Swin transformer" paper.

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published