Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 7
Articles | Volume 15, issue 7
https://doi.org/10.5194/gmd-15-3121-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-3121-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 7
Development and technical paper
 | 
18 Apr 2022
Development and technical paper |  | 18 Apr 2022

Predicting global terrestrial biomes with the LeNet convolutional neural network

Hisashi Sato and Takeshi Ise

Viewed

Total article views: 4,208 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
3,310 808 90 4,208 250 53 56
  • HTML: 3,310
  • PDF: 808
  • XML: 90
  • Total: 4,208
  • Supplement: 250
  • BibTeX: 53
  • EndNote: 56
Views and downloads (calculated since 11 Aug 2021)
Cumulative views and downloads (calculated since 11 Aug 2021)

Viewed (geographical distribution)

Total article views: 4,208 (including HTML, PDF, and XML) Thereof 3,897 with geography defined and 311 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 14 Jan 2025
Download
Short summary
Accurately predicting global coverage of terrestrial biome is one of the earliest ecological concerns, and many empirical schemes have been proposed to characterize their relationship. Here, we demonstrate an accurate and practical method to construct empirical models for operational biome mapping via a convolutional neural network (CNN) approach.
Read more