13 citations · 27 across the 4 of their papers we have counts for
6 papers
MAESTRO: Masked AutoEncoders for Multimodal, Multitemporal, and Multispectral Earth Observation Data
Antoine Labatie, Michael Vaccaro, Nina Lardiere +2
Self-supervised learning holds great promise for remote sensing, but standard self-supervised methods must be adapted to the unique characteristics of Earth observation data. We ta…
FLAIR-HUB: Large-scale Multimodal Dataset for Land Cover and Crop Mapping
Anatol Garioud, Sébastien Giordano, Nicolas David +1
The growing availability of high-quality Earth Observation (EO) data enables accurate global land cover and crop type monitoring. However, the volume and heterogeneity of these dat…
FLAIR: a Country-Scale Land Cover Semantic Segmentation Dataset From Multi-Source Optical Imagery
Anatol Garioud, Nicolas Gonthier, Loic Landrieu +5
We introduce the French Land cover from Aerospace ImageRy (FLAIR), an extensive dataset from the French National Institute of Geographical and Forest Information (IGN) that provide…
FLAIR #2: textural and temporal information for semantic segmentation from multi-source optical imagery
Anatol Garioud, Apolline De Wit, Marc Poupée +3
The FLAIR #2 dataset hereby presented includes two very distinct types of data, which are exploited for a semantic segmentation task aimed at mapping land cover. The data fusion wo…
GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates
Valerio Marsocci, Nicolas Gonthier, Anatol Garioud +2
Land cover maps are a pivotal element in a wide range of Earth Observation (EO) applications. However, annotating large datasets to develop supervised systems for remote sensing (R…
FLAIR #1: semantic segmentation and domain adaptation dataset
Anatol Garioud, Stéphane Peillet, Eva Bookjans +2
The French National Institute of Geographical and Forest Information (IGN) has the mission to document and measure land-cover on French territory and provides referential geographi…