论文标题
FLAIR#1:语义细分和域适应数据集
FLAIR #1: semantic segmentation and domain adaptation dataset
论文作者
论文摘要
法国国家地理和森林信息研究所(IGN)的使命是记录和测量法国领土上的土地覆盖,并提供参考地理数据集,包括高分辨率的航空图像和地形图。对土地覆盖的监测在土地管理和规划计划中起着至关重要的作用,这可能会产生重大的社会经济和环境影响。与遥感技术一起,人工智能(IA)有望成为确定土地覆盖及其进化的强大工具。 IGN目前正在探索IA在生产高分辨率土地覆盖地图中的潜力。值得注意的是,采用深度学习方法来获得空中图像的语义分割。但是,像法国一样大的领土暗示着异质的环境:景观和图像获取的变化使得在整个法国提供统一,可靠和准确的结果变得具有挑战性。提出的“天赋”数据集是IGN当前使用的数据集的一部分,以建立法国国家参考土地覆盖地图“占领dusolàgrande”échelle(OCS-GE)。
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 à grande échelle" (OCS- GE).