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Delobel, LA, Moffat, D, Tebbs, E and Baas, ACW 2025 Segmenting and characterising ripple patterns on sand dunes using machine learning and 2D semi-variogram. Remote Sensing of Environment, 331. 115031. 10.1016/j.rse.2025.115031
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Abstract/Summary
Sand ripples, shaped by fluid flow like wind or water, are common on dunes on Earth and Mars. Their patterns reveal local transport conditions, offering insights into wind regimes where direct observations are lacking. Since manual mapping is slow and subjective, automated methods are essential for consistent large-scale analysis. This study presents two novel and complementary methods for mapping ripple patterns on Martian dunes using high-resolution imagery: a U-Net model for pattern classification and a 2D semi-variogram for measuring ripple spacing and orientation. Tested on 42 barchan dunes across six Martian regions, the U-Net showed reliable ripple classification (F1-score 79 %), while the variogram method achieved high accuracy for ripple spacing (R2 = 0.78) and orientation (R2 = 0.98). Together, these approaches enable efficient, large-scale analysis of ripples for sediment transport on any planetary surface and can be applied to other patterned features.
| Item Type: | Publication - Article |
|---|---|
| Additional Keywords: | Texture segmentation U-net model Spatial autocorrelation HiRISE Mars |
| Divisions: | Plymouth Marine Laboratory > National Capability categories > NERC EO Data Analysis and AI Service (NEODAAS) Plymouth Marine Laboratory > Science Areas > Earth Observation Science and Applications (expired) Plymouth Marine Laboratory > Science Areas > Environmental Intelligence |
| Depositing User: | S Hawkins |
| Date made live: | 23 Sep 2025 10:38 |
| Last Modified: | 23 Sep 2025 10:38 |
| URI: | https://plymsea.ac.uk/id/eprint/10517 |
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