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).
翻译:法国国家地理和森林信息研究所的任务是记录和测量法国领土上的土地覆盖情况,并提供优惠地理数据集,包括高分辨率的航空图像和地形图;监测土地覆盖情况在土地管理和规划举措中发挥着关键作用,可产生重大的社会经济和环境影响;与遥感技术一道,人工智能(IA)有望成为确定土地覆盖情况及其演变的有力工具;国际地理研究所目前正在探索国际海洋协会在制作高分辨率土地覆盖图方面的潜力;值得注意的是,采用了深层学习方法,以获得空中图像的语义分解;然而,法国所隐含的多种情况:地貌和图像获取的变化,使得整个法国难以提供统一、可靠和准确的结果;FLAIR-one数据集是法国地理研究所目前用来绘制法国国家参考土地覆盖图“Occupation du sol çagle gene ne'echelle”(OCS-GE)的部分数据。