10m Annual Land Use Land Cover (9-class)

This dataset, produced by Impact Observatory, 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 ...本数据集由Impact Observatory、微软（Microsoft）与艾斯瑞（Esri）联合制作，展示了2017至2023年间基于欧洲空间局（European Space Agency, ESA）Sentinel-2影像、分辨率为10米的全球土地利用与土地覆盖（Land Use and Land Cover, LULC）地图。每张地图均为全年9类土地利用与土地覆盖预测结果的合成图，用以生成对应年份的典型地表快照。本数据集由Impact Observatory生成，该机构借助由美国国家地理学会（National Geographic Society）精心整理的数十亿人工标注像素点，训练了用于地表分类的深度学习模型。每张全球地图均通过将该模型应用于……生成。

搜集汇总

背景与挑战

背景概述

该数据集是由Impact Observatory、Microsoft和Esri合作制作的全球土地利用和土地覆盖（LULC）地图，基于ESA Sentinel-2影像，覆盖2017-2023年，分辨率为10米，包含9个类别。其特点包括使用深度学习模型生成，平均准确率超过75%，并优化了类别间的异常变化，数据每年更新，可通过多种方式访问。