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

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Cited articles

Aario, R.: Classification and terminology of morainic landforms in Finland, Boreas, 6, 87–100, https://doi.org/10.1111/j.1502-3885.1977.tb00338.x, 1977. 
Ali, A., Dunlop, P., Coleman, S., Kerr, D., McNabb, R. W., and Noormets, R.: Glacier area changes in the Arctic and high latitudes using satellite remote sensing, J. Maps, 19, 1–7, https://doi.org/10.1080/17445647.2023.2247416, 2023. 
Aydda, A., Althuwaynee, O. F., and Pokharel, B.: An easy method for barchan dunes automatic extraction from multispectral satellite data, IOP Conf. Ser. Earth Environ. Sci., 419, 012015, https://doi.org/10.1088/1755-1315/419/1/012015, 2020. 
Barnes, R.: RichDEM: terrain Analysis Software, GitHub [code], https://github.com/r-barnes/richdem (last access: 3 June 2023), 2016. 
Barnes, T. and Filhol, S.: Aeteia/Ribbed-Moraine: Release ver.8.3 for ribbed moraines detection script, Zenodo [code and data set], https://doi.org/10.5281/zenodo.7991094, 2023. 
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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.
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