We introduce the French Land cover from Aerospace ImageRy (FLAIR), an extensive dataset from the French National Institute of Geographical and Forest Information (IGN) that provides a unique and rich resource for large-scale geospatial analysis. FLAIR contains high-resolution aerial imagery with a ground sample distance of 20 cm and over 20 billion individually labeled pixels for precise land-cover classification. The dataset also integrates temporal and spectral data from optical satellite time series. FLAIR thus combines data with varying spatial, spectral, and temporal resolutions across over 817 km2 of acquisitions representing the full landscape diversity of France. This diversity makes FLAIR a valuable resource for the development and evaluation of novel methods for large-scale land-cover semantic segmentation and raises significant challenges in terms of computer vision, data fusion, and geospatial analysis. We also provide powerful uni- and multi-sensor baseline models that can be employed to assess algorithm's performance and for downstream applications. Through its extent and the quality of its annotation, FLAIR aims to spur improvements in monitoring and understanding key anthropogenic development indicators such as urban growth, deforestation, and soil artificialization. Dataset and codes can be accessed at https://ignf.github.io/FLAIR/

翻译：本文介绍法国航空航天影像土地覆盖（FLAIR）数据集，该数据集由法国国家地理与森林信息研究所（IGN）创建，为大规模地理空间分析提供了独特且丰富的资源。FLAIR包含空间分辨率达20厘米的高分辨率航拍影像，以及超过200亿个独立标注像素，以实现精确的土地覆盖分类。该数据集还整合了光学卫星时间序列的时空与光谱数据。因此，FLAIR融合了覆盖法国全境景观多样性的817平方公里以上采集区域中具备不同空间、光谱与时间分辨率的数据。这种多样性使FLAIR成为开发与评估大规模土地覆盖语义分割新方法的宝贵资源，并在计算机视觉、数据融合与地理空间分析领域提出了重大挑战。我们还提供了强大的单传感器与多传感器基线模型，可用于评估算法性能及下游应用。凭借其庞大规模与高质量的标注，FLAIR旨在推动对城市扩张、森林砍伐与土壤人工化等关键人为发展指标的监测与理解。数据集及代码可通过https://ignf.github.io/FLAIR/获取。