Earth Surface Dynamics
Earth Surface Dynamics
ESurf
Articles | Volume 12, issue 3
Articles | Volume 12, issue 3
https://doi.org/10.5194/esurf-12-765-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esurf-12-765-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 12, issue 3
Research article
 | 
17 May 2024
Research article |  | 17 May 2024

Evaluating the accuracy of binary classifiers for geomorphic applications

Matthew William Rossi

Viewed

Total article views: 2,969 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,201 662 106 2,969 137 170
  • HTML: 2,201
  • PDF: 662
  • XML: 106
  • Total: 2,969
  • BibTeX: 137
  • EndNote: 170
Views and downloads (calculated since 02 Nov 2022)
Cumulative views and downloads (calculated since 02 Nov 2022)

Viewed (geographical distribution)

Total article views: 2,969 (including HTML, PDF, and XML) Thereof 2,852 with geography defined and 117 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 06 May 2026
Download
Short summary
Accurately identifying the presence and absence of landforms is important to inferring processes and testing numerical models of landscape evolution. Using synthetic scenarios, I show that the Matthews correlation coefficient (MCC) should be favored over the F1 score when comparing accuracy across scenes where landform abundances vary. Despite the resilience of MCC to imbalanced data, strong sensitivity to the size and shape of features can still occur when truth and model data are misaligned.
Read more
Share