该数据集为全球年度土地利用与土地覆盖（LULC）时间序列地图，当前包含2017–2023年数据。地图基于欧空局（ESA）Sentinel-2影像生成，空间分辨率为10米。每幅年度地图由全年9类LULC预测结果合成，以表征该年度典型地表覆盖状态。本数据集由Impact Observatory、Microsoft与Esri联合制作，呈现2017–2023年间基于ESA Sentinel-2影像的全球10米分辨率LULC分布图。Impact Observatory利用数十亿经国家地理学会（National Geographic Society）人工标注并整理的像素样本，训练深度学习模型进行土地分类；各年度全球地图均通过将该模型应用于Microsoft Planetary Computer平台提供的Sentinel-2年度场景集合生成。每幅地图经评估的平均精度超过75%。相较Impact Observatory此前发布的[版本](https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class)，本版在类别间异常变化（尤其是“裸地”与“林地”“农作物”“淹没植被”“牧场”等植被类之间）方面实现了相对减少，并重新配准至ESA Sentinel-2影像所采用的UTM分幅网格。全部年份数据均采用知识共享署名4.0国际许可（Creative Commons BY-4.0）发布。

Time series of annual global maps of land use and land cover (LULC). It currently has data from 2017-2023. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year. This dataset, produced by [Impact Observatory](http://impactobservatory.com/), Microsoft, and Esri, displays a global map of land use and land cover (LULC) derived from ESA Sentinel-2 imagery at 10 meter resolution for the years 2017 - 2023. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year. This dataset was generated by Impact Observatory, which used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. Each global map was produced by applying this model to the Sentinel-2 annual scene collections from the Mircosoft Planetary Computer. Each of the maps has an assessed average accuracy of over 75%. These maps have been improved from Impact Observatory’s [previous release](https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class) and provide a relative reduction in the amount of anomalous change between classes, particularly between “Bare” and any of the vegetative classes “Trees,” “Crops,” “Flooded Vegetation,” and “Rangeland”. This updated time series of annual global maps is also re-aligned to match the ESA UTM tiling grid for Sentinel-2 imagery. All years are available under a Creative Commons BY-4.0.