Deep transfer learning to localise soil spectroscopic modelling

Online, AGU Fall Meeting, 12/12/2022.

Presented a Deep Transfer Learning solutions for decades-old challenge in soil spectroscopic modelling, i.e. localising spectroscopic models with large spectral libraries, at the world’s largest Earth Science conference, AGU Fall Meeting.

The accepted abstract can be found here and the related paper (Shen et al., 2022)

References

2022

P&RS

Deep transfer learning of global spectra for local soil carbon monitoring

Zefang Shen, Leonardo Ramirez-Lopez, Thorsten Behrens, and 8 more authors

ISPRS Journal of Photogrammetry and Remote Sensing, 2022

Bib HTML

@article{shen2022deep,
  title = {Deep transfer learning of global spectra for local soil carbon monitoring},
  author = {Shen, Zefang and Ramirez-Lopez, Leonardo and Behrens, Thorsten and Cui, Lei and Zhang, Mingxi and Walden, Lewis and Wetterlind, Johanna and Shi, Zhou and Sudduth, Kenneth A and Baumann, Philipp and others},
  journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
  volume = {188},
  pages = {190--200},
  year = {2022},
  publisher = {Elsevier},
}