Anatol Garioud, Stéphane Peillet, Eva Bookjans, Sébastien Giordano, Boris Wattrelos
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).
| Task | Dataset | Metric | Value | Model |
|---|---|---|---|---|
| Semantic Segmentation | FLAIR (French Land cover from Aerospace ImageRy) | mIoU | 0.557 | U-Net baseline |
| 10-shot image generation | FLAIR (French Land cover from Aerospace ImageRy) | mIoU | 0.557 | U-Net baseline |