52 citations · 69 across the 9 of their papers we have counts for
10 papers
OceanSAR-2: A Universal Feature Extractor for SAR Ocean Observation
Alexandre Tuel, Thomas Kerdreux, Quentin Febvre +6
We present OceanSAR-2, the second generation of our foundation model for SAR-based ocean observation. Building on our earlier release, which pioneered self-supervised learning on S…
Efficient Self-Supervised Learning for Earth Observation via Dynamic Dataset Curation
Thomas Kerdreux, Alexandre Tuel, Quentin Febvre +2
Self-supervised learning (SSL) has enabled the development of vision foundation models for Earth Observation (EO), demonstrating strong transferability across diverse remote sensin…
OceanBench: The Sea Surface Height Edition
J. Emmanuel Johnson, Quentin Febvre, Anastasia Gorbunova +4
The ocean profoundly influences human activities and plays a critical role in climate regulation. Our understanding has improved over the last decades with the advent of satellite…
Training neural mapping schemes for satellite altimetry with simulation data
Quentin Febvre, Julien Le Sommer, Clément Ubelmann +1
Satellite altimetry combined with data assimilation and optimal interpolation schemes have deeply renewed our ability to monitor sea surface dynamics. Recently, deep learning (DL)…
Scale-aware neural calibration for wide swath altimetry observations
Quentin Febvre, Clément Ubelmann, Julien Le Sommer +1
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…
Learning Neural Optimal Interpolation Models and Solvers
Maxime Beauchamp, Joseph Thompson, Hugo Georgenthum +2
The reconstruction of gap-free signals from observation data is a critical challenge for numerous application domains, such as geoscience and space-based earth observation, when th…
4DVarNet-SSH: end-to-end learning of variational interpolation schemes for nadir and wide-swath satellite altimetry
Maxime Beauchamp, Quentin Febvre, Hugo Georgentum +1
The reconstruction of sea surface currents from satellite altimeter data is a key challenge in spatial oceanography, especially with the upcoming wide-swath SWOT (Surface Ocean and…
Multimodal 4DVarNets for the reconstruction of sea surface dynamics from SST-SSH synergies
Ronan Fablet, Quentin Febvre, Bertrand Chapron
Due to the irregular space-time sampling of sea surface observations, the reconstruction of sea surface dynamics is a challenging inverse problem. While satellite altimetry provide…
Joint calibration and mapping of satellite altimetry data using trainable variational models
Quentin Febvre, Ronan Fablet, Julien Le Sommer +1
Satellite radar altimeters are a key source of observation of ocean surface dynamics. However, current sensor technology and mapping techniques do not yet allow to systematically r…
Reversible designs for extreme memory cost reduction of CNN training
Tristan Hascoet, Quentin Febvre, Yasuo Ariki +1
Training Convolutional Neural Networks (CNN) is a resource intensive task that requires specialized hardware for efficient computation. One of the most limiting bottleneck of CNN t…