Articles | Volume 18
https://doi.org/10.5194/asr-18-65-2021
https://doi.org/10.5194/asr-18-65-2021
11 May 2021
 | 11 May 2021

Using machine learning to produce a very high resolution land-cover map for Ireland

Eoin Walsh, Geoffrey Bessardon, Emily Gleeson, and Priit Ulmas

Viewed

Total article views: 6,122 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
5,507 558 57 6,122 59 56
  • HTML: 5,507
  • PDF: 558
  • XML: 57
  • Total: 6,122
  • BibTeX: 59
  • EndNote: 56
Views and downloads (calculated since 11 May 2021)
Cumulative views and downloads (calculated since 11 May 2021)

Viewed (geographical distribution)

Total article views: 5,833 (including HTML, PDF, and XML) Thereof 5,833 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 13 Dec 2024
Download
Short summary
In this work, machine learning techniques, satellite data and land-cover data were used to produce a land-cover map for Ireland that shows greater accuracy and resolution than an altered version of the standard land-cover map (ECOCLIMAP-SG) used for numerical weather prediction. This method offers a way to universally improve meteorological land-cover maps across jurisdictions, while also offering a method of updating the map regularly to account for seasonal changes in surface land-covers.