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：法国国家地理与森林信息研究所（IGN）的使命是记录和测量法国领土的土地覆盖情况，并提供包括高分辨率航空影像和地形图在内的参考地理数据集。土地覆盖监测在土地管理和规划举措中发挥着关键作用，这些举措可能产生重大的社会经济和环境影响。结合遥感技术，人工智能有望成为确定土地覆盖及其演变的有力工具。IGN目前正在探索人工智能在高分辨率土地覆盖图生产中的潜力，特别是采用深度学习方法对航空影像进行语义分割。然而，像法国这样广阔的领土涉及异质化背景：景观差异和图像采集特点使得在全法国范围内提供统一、可靠且准确的结果面临挑战。本文介绍的FLAIR-one数据集是IGN目前用于建立法国国家参考土地覆盖图"大比例尺土地覆盖"（OCS-GE）的数据集组成部分。