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The French National Institute of Geographical and Forest Information (IGN)has the mission to document and measure land-cover on French territory andprovides referential geographical datasets, including high-resolution aerialimages and topographic maps. The monitoring of land-cover plays a crucial rolein land management and planning initiatives, which can have significantsocio-economic and environmental impact. Together with remote sensingtechnologies, artificial intelligence (IA) promises to become a powerful toolin determining land-cover and its evolution. IGN is currently exploring thepotential of IA in the production of high-resolution land cover maps. Notably,deep learning methods are employed to obtain a semantic segmentation of aerialimages. However, territories as large as France imply heterogeneous contexts:variations in landscapes and image acquisition make it challenging to provideuniform, reliable and accurate results across all of France. The FLAIR-onedataset presented is part of the dataset currently used at IGN to establish theFrench national reference land cover map "Occupation du sol \`a grande\'echelle" (OCS- GE).

FLAIR #1: semantic segmentation and domain adaptation dataset