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arXiv · 2211.12979

FLAIR #1: semantic segmentation and domain adaptation dataset

Anatol Garioud·Stéphane Peillet·Eva Bookjans·Sébastien Giordano·Boris Wattrelos
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Abstract

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 geographical datasets, including high-resolution aerial images and topographic maps. The monitoring of land-cover plays a crucial role in land management and planning initiatives, which can have significant socio-economic and environmental impact. Together with remote sensing technologies, artificial intelligence (IA) promises to become a powerful tool in determining land-cover and its evolution. IGN is currently exploring the potential of IA in the production of high-resolution land cover maps. Notably, deep learning methods are employed to obtain a semantic segmentation of aerial images. However, territories as large as France imply heterogeneous contexts: variations in landscapes and image acquisition make it challenging to provide uniform, reliable and accurate results across all of France. The FLAIR-one dataset presented is part of the dataset currently used at IGN to establish the French national reference land cover map "Occupation du sol \`a grande \'echelle" (OCS- GE).

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Anatol Garioud, Stéphane Peillet, Eva Bookjans, Sébastien Giordano, Boris Wattrelos. 2022-11-23. FLAIR #1: semantic segmentation and domain adaptation dataset. https://doi.org/10.13140/rg.2.2.30183.73128/1

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