Articles | Volume 12, issue 3
https://doi.org/10.5194/esurf-12-801-2024
https://doi.org/10.5194/esurf-12-801-2024
Research article
 | 
10 Jun 2024
Research article |  | 10 Jun 2024

A machine learning approach to the geomorphometric detection of ribbed moraines in Norway

Thomas J. Barnes, Thomas V. Schuler, Simon Filhol, and Karianne S. Lilleøren

Download

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2430', Paul Dunlop, 12 Jan 2024
  • RC2: 'Comment on egusphere-2023-2430', Anonymous Referee #2, 28 Mar 2024
  • AC1: 'Response to reviewers on egusphere-2023-2430', Thomas Barnes, 12 Apr 2024

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Thomas Barnes on behalf of the Authors (12 Apr 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (29 Apr 2024) by Giulia Sofia
ED: Publish as is (29 Apr 2024) by Tom Coulthard (Editor)
AR by Thomas Barnes on behalf of the Authors (01 May 2024)
Download
Short summary
In this paper, we use machine learning to automatically outline landforms based on their characteristics. We test several methods to identify the most accurate and then proceed to develop the most accurate to improve its accuracy further. We manage to outline landforms with 65 %–75 % accuracy, at a resolution of 10 m, thanks to high-quality/high-resolution elevation data. We find that it is possible to run this method at a country scale to quickly produce landform inventories for future studies.