<figure><img src="image_1.png" /></figure>

COLLEGE
DOCTORAL
PAYS DE LA LOIRE
ED MATIERE MOLECULES
MATERIAUX & GEOSCIENCES

# Deep Learning:

## A new tool for mapping and analysis of dune fields

(e.g. Rub'Al Khali sand sea)

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## Authors

J. Daynac*<sup>a</sup>, P.Bessin<sup>a</sup>, S.Pochat<sup>b</sup>, R.Mourgues<sup>a</sup>,
<sup>a</sup>LPG UMR 6112, Le Mans Université, France
<sup>b</sup>LPG UMR 6112, Nantes Université, France

## Contact

jimmy.daynac@univ-lemans.fr

## Acknowledgements

Thanks to Samuel Shumack for our collaboration. This work was supported by the French National program PNP (Programme National de Planétologie).

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## INTRODUCTION & PROBLEMATIC

Geometry and spatial organisation of dunes are controlled by the interaction between a moving fluid (shear stress) and granular sediments (quantity, size and shape of particles). Dunes are ubiquitous on planetary surfaces, shaping repetitive patterns at different scales. Spatial and volumetric mapping and analysis of these bedforms is therefore essential to better understand i) their formation processes and ii) their spatio-temporal evolution. Bedform mapping studies are usually focused either on a dune field scale (i.e. main geometric patterns) or on a few selected dunes (i.e slope, outline, crestlines, etc.), but never encompassed both scale. Indeed, numerous maps are produced by manual digitizing (e.g. crestlines, outlines, slip faces) which is very time-consuming and biased by mapper subectivity.

Here, we propose an automatic method of dune outlines, crestlines and slip face mapping from DEM based on Artificial Intelligence (AI) which allows instantaneous, massive and integrated extraction of several properties from local scale (each single dune) to regional scale (dune field). We illustrate our AI protocol capabilities, reproducibility and accuracy on the Rub'Al Khali sand sea.

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<figure><img src="image_5.png" /><figcaption>Simple linear dunes</figcaption></figure>

<caption>Barchanoid dunes</caption>

## RUB'AL KHALI AUTOMATIC MAPPING & MORPHOMETRIC PARAMETERS

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Geographic projection system: WGS84/UTM 40N

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## RESULTS

Metrical performances of the protocol from manual references

<table><thead><tr><th>Metric</th><th>Recall (%)</th><th>Accuracy (%)</th><th>Quality (%)</th><th>Crest deviation (°)</th></tr></thead><tbody><tr><td>Mean</td><td>87,1</td><td>90,9</td><td>69,1</td><td>11</td></tr><tr><td>Median</td><td>92</td><td>91,7</td><td>69,4</td><td>8</td></tr></tbody></table>

### Deep Learning performances

After the DNN training, the mapping model is assessed from 20% of the samples, demonstrating good performances: 92% of precision, 87% of recall and 70% of mapping quality.

### Crest location & layout

Automatically extracted crestlines show low directional dispersion (11°) regarding manually digitized ones (considered as "ground truth").

### Morphometric features

We automatically mapped more than 78,000 dune outlines and crestlines, on an area of more than 58,000 km². This is the first map ever of width, length, height and orientation of 78,000 dunes of the Rub'Al Khali desert, showing:

- Mean crestlines direction of 81° (range: 45°-135°) for high dunes (>70m) and of 35° (range: 171°-180°) for smaller dunes (<70m);

- Dune widths changes from 400 m to 1.8 km following a S to N gradient;

- A central area with a very consistent pattern of wide (>1.2 km), very long (>2 km) and high (>70 m) dunes;

- A south area with a very consistent pattern of narrower (<600 m), shorter (< 1 km) and a height gradient that decreases to the south (70 to 20 m);

### Consistency with previous studies

Our detailed morphometric measurements at the dune field scale shows:

- between the central area pattern of our work and the megabarchan area identified in dune field scale previous studies;

- between the south area pattern of our work and seif dunes area identified in dune field scale previous studies.

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modified from Edgell, 2006

## CONCLUSION & FURTHER WORKS

This protocol demonstrate good performances to map various and complex dune forms. This work is an original production that completes atlases of this region present in the literature the morphological boundaries mapped from aerial/satellite images (Barth, 2001; Glennie, 1970; McKee, 1979).

Only a few detection problems were noticed, and these were caused on by the DEM's resolution limit in a few specific locations, which produced a weak dune signal. This protocol may be used in the future on hydraulic and extraterrestrial dunes.

<figure><img src="image_10.png" /><figcaption>RR & GT mask</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>error prediction</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>Automatic crest output</figcaption></figure>

<figure><img src="image_13.png" /></figure>