6 citations · 9 across the 7 of their papers we have counts for
5 papers · 2 filters
PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Models
Valerio Marsocci, Yuru Jia, Georges Le Bellier +12
Geospatial Foundation Models (GFMs) have emerged as powerful tools for extracting representations from Earth observation data, but their evaluation remains inconsistent and narrow.…
GalLoP: Learning Global and Local Prompts for Vision-Language Models
Marc Lafon, Elias Ramzi, Clément Rambour +2
Prompt learning has been widely adopted to efficiently adapt vision-language models (VLMs), e.g. CLIP, for few-shot image classification. Despite their success, most prompt learnin…
Cross-sensor self-supervised training and alignment for remote sensing
Valerio Marsocci, Nicolas Audebert
Large-scale ''foundation models'' have gained traction as a way to leverage the vast amounts of unlabeled remote sensing data collected every day. However, due to the multiplicity…
Cross-sensor super-resolution of irregularly sampled Sentinel-2 time series
Aimi Okabayashi, Nicolas Audebert, Simon Donike +1
Satellite imaging generally presents a trade-off between the frequency of acquisitions and the spatial resolution of the images. Super-resolution is often advanced as a way to get…
Detecting Out-Of-Distribution Earth Observation Images with Diffusion Models
Georges Le Bellier, Nicolas Audebert
Earth Observation imagery can capture rare and unusual events, such as disasters and major landscape changes, whose visual appearance contrasts with the usual observations. Deep mo…