Výsledky vyhledávání - "Ubelmann, A."

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Detecting Looted Archaeological Sites from Satellite Image Time Series

Autor: Vincent, Elliot, Saroufim, Mehraïl, Chemla, Jonathan, Ubelmann, Yves, Marquis, Philippe, Ponce, Jean, Aubry, Mathieu

Archaeological sites are the physical remains of past human activity and one of the main sources of information about past societies and cultures. However, they are also the target of malevolent human actions, especially in countries having experienc

Externí odkaz: http://arxiv.org/abs/2409.09432

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Data-Driven Calibration of SWOT's Systematic Errors: First In-Flight Assessment.

Autor: Ubelmann, Clément¹ (AUTHOR) clement.ubelmann@datlas.fr, Dibarboure, Gérald² (AUTHOR), Flamant, Benjamin³ (AUTHOR), Delepoulle, Antoine³ (AUTHOR), Vayre, Maxime³ (AUTHOR), Faugère, Yannice² (AUTHOR), Prandi, Pierre³ (AUTHOR), Raynal, Matthias² (AUTHOR), Briol, Frédéric³ (AUTHOR), Bracher, Geoffroy³ (AUTHOR), Cadier, Emeline³ (AUTHOR)

Publikováno v: Remote Sensing. Oct2024, Vol. 16 Issue 19, p3558. 21p.

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Akademický článek

Save the Syrian Heritage: technologies to document Palmyra and endangered world heritage Interview to Yves Ubelmann, Iconem's CEO

Autor: Redazione MediaGEO

Publikováno v: Archeomatica, Vol 8, Iss 3 (2018)

An interview to ICONEM on the role of digital technologies used to preserve endangered cultural heritage, with a particular eye on syrian cultural heritage. Still at the moment the French start-up is working directly on the field to preserve monument

Externí odkaz: https://doaj.org/article/c497cf5ba9ec48c4823de64f75f716d1

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Training neural mapping schemes for satellite altimetry with simulation data

Autor: Febvre, Quentin, Sommer, Julien Le, Ubelmann, Clément, Fablet, Ronan

Satellite altimetry combined with data assimilation and optimal interpolation schemes have deeply renewed our ability to monitor sea surface dynamics. Recently, deep learning (DL) schemes have emerged as appealing solutions to address space-time inte

Externí odkaz: http://arxiv.org/abs/2309.14350

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Scale-aware neural calibration for wide swath altimetry observations

Autor: Febvre, Quentin, Ubelmann, Clément, Sommer, Julien Le, Fablet, Ronan

Sea surface height (SSH) is a key geophysical parameter for monitoring and studying meso-scale surface ocean dynamics. For several decades, the mapping of SSH products at regional and global scales has relied on nadir satellite altimeters, which prov

Externí odkaz: http://arxiv.org/abs/2302.04497

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Akademický článek

Training Neural Mapping Schemes for Satellite Altimetry With Simulation Data

Autor: Q. Febvre, J. Le Sommer, C. Ubelmann, R. Fablet

Publikováno v: Journal of Advances in Modeling Earth Systems, Vol 16, Iss 7, Pp n/a-n/a (2024)

Abstract Satellite altimetry combined with data assimilation and optimal interpolation schemes have deeply renewed our ability to monitor sea surface dynamics. Recently, deep learning schemes have emerged as appealing solutions to address space‐tim

Externí odkaz: https://doaj.org/article/f46edd62664b4995ab9f38b2cbf95bce

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A DNN Framework for Learning Lagrangian Drift With Uncertainty

Autor: Jenkins, Joseph, Paiement, Adeline, Ourmières, Yann, Sommer, Julien Le, Verron, Jacques, Ubelmann, Clément, Glotin, Hervé

Reconstructions of Lagrangian drift, for example for objects lost at sea, are often uncertain due to unresolved physical phenomena within the data. Uncertainty is usually overcome by introducing stochasticity into the drift, but this approach require

Externí odkaz: http://arxiv.org/abs/2204.05891

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