Sentinel2GlobalLULC: A dataset of Sentinel-2 georeferenced RGB imagery acquired between June 2015 and October 2020 annotated for global land use/land cover mapping with deep learning (License CC BY 4.0)

Sentinel2GlobalLULC is a deep learning-ready dataset of RGB images from the Sentinel-2 satellites designed for global land use and land cover (LULC) mapping. Sentinel2GlobalLULC v2.0 contains 194,877 images in GeoTiff and JPEG format corresponding to 29 broad LULC classes. Each image has 224 x 224 pixels at 10 m spatial resolution and was produced by assigning the 25th percentile of all available observations in the Sentinel-2 collection between June 2015 and October 2020 in order to remove atmospheric effects (i.e., clouds, aerosols, shadows, snow, etc.). A spatial purity value was assigned to each image based on the consensus across 15 different global LULC products available in Google Earth Engine (GEE). Our dataset is structured into 3 main zip-compressed folders, an Excel file with a dictionary for class names and descriptive statistics per LULC class, and a python script to convert RGB GeoTiff images into JPEG format. The first folder contains 29 zip-compressed subfolders where each one corresponds to a specific LULC class with hundreds to thousands of GeoTiff Sentinel-2 RGB images. The second folder contains 29 zip-compressed subfolders with a JPEG formatted version of the same images provided in the first main folder. The third folder includes 29 zip-compressed CSV files with as many rows as images and with 12 columns containing the following metadata (this same metadata is provided in the image filenames): Land Cover Class ID: is the identification number of each LULC class Land Cover Class Short Name: is the short name of each LULC class Pixel purity Value: is the spatial purity of each pixel for its corresponding LULC class calculated as the spatial consensus across up to 15 land-cover products Image ID: is the identification number of each image within its corresponding LULC class GHM Value: is the spatial average of the Global Human Modification index (gHM) for each image Latitude: is the latitude of the center point of each image Longitude: is the longitude of the center point of each image Country Code: is the Alpha-2 country code of each image as described in the ISO 3166 international standard. To understand the country codes, we recommend the user to visit the following website where they present the Alpha-2 code for each country as described in the ISO 3166 international standard:https: //www.iban.com/country-codes Administrative Department Level1: is the administrative level 1 name to which each image belongs Administrative Department Level2: is the administrative level 2 name to which each image belongs Locality: is the name of the locality to which each image belongs Number of S2 images : is the number of found instances in the corresponding Sentinel-2 image collection between June 2015 and October 2020, when aggregated and exported as a final image For seven LULC classes, we could not export from GEE all images that fulfilled a spatial purity of 100% since there were millions of them. In this case, we exported a stratified random sample of 14,000 images and provided an additional CSV file with the images actually contained in our dataset. That is, for these seven LULC classes, we provide these 2 CSV files: A CSV file that contains all exported images for this class A CSV file that contains all images available for this class at spatial purity of 100%, both the ones exported and the ones not exported, in case the user wants to export them. These CSV filenames end with "including_non_downloaded_images". © Sentinel2GlobalLULC Dataset by Yassir Benhammou, Domingo Alcaraz-Segura, Emilio Guirado, Rohaifa Khaldi, Boujemâa Achchab, Francisco Herrera & Siham Tabik is marked with Attribution 4.0 International (CC-BY 4.0)Sentinel2GlobalLULC是一款专为全球土地利用与土地覆盖（Land Use and Land Cover, LULC）制图打造的、适配深度学习的哨兵二号（Sentinel-2）卫星RGB图像数据集。 Sentinel2GlobalLULC v2.0版本包含194,877张GeoTiff与JPEG格式图像，涵盖29个宽泛的LULC类别。每张图像空间分辨率为10米，尺寸为224×224像素，其生成逻辑为：选取2015年6月至2020年10月间哨兵二号影像集合中所有可用观测值的第25百分位数，以此消除大气干扰（如云、气溶胶、阴影、积雪等）。 研究团队基于谷歌地球引擎（Google Earth Engine, GEE）中15种不同的全球LULC产品的共识结果，为每张图像赋予空间纯度值。本数据集的组成结构如下：3个主要的压缩文件夹、1个包含类别名称字典与各LULC类别描述性统计量的Excel文件，以及1段用于将RGB格式GeoTiff图像转换为JPEG格式的Python脚本。 第一个主文件夹包含29个压缩子文件夹，每个子文件夹对应一个特定的LULC类别，内含数百至数千张哨兵二号RGB格式GeoTiff图像。第二个主文件夹同样包含29个压缩子文件夹，存储与第一个主文件夹中完全一致的图像的JPEG格式版本。第三个主文件夹包含29个压缩CSV文件，每个文件的行数与对应类别的图像总数一致，共包含12列元数据（此类元数据同样内嵌于图像文件名中）： - 土地覆盖类别ID：各LULC类别的唯一识别编号 - 土地覆盖类别简称：各LULC类别的简短命名 - 像素纯度值：针对对应LULC类别的单像素空间纯度，通过最多15种土地覆盖产品的空间共识计算得出 - 图像ID：对应LULC类别内单张图像的专属识别编号 - GHM值：单张图像的全球人类改造指数（Global Human Modification index, gHM）空间平均值 - 纬度：单张图像中心点的纬度坐标 - 经度：单张图像中心点的经度坐标 - 国家代码：符合ISO 3166国际标准的Alpha-2国家代码。如需查询对应国家的Alpha-2代码，建议用户访问以下网站：https://www.iban.com/country-codes - 一级行政区：单张图像所属的一级行政区域名称 - 二级行政区：单张图像所属的二级行政区域名称 - 聚居地名称：单张图像所属的聚居地名称 - 哨兵二号影像数量：2015年6月至2020年10月间，对应哨兵二号影像集合中被检索到的有效实例总数，经聚合处理后导出为最终图像 针对7个LULC类别，由于符合100%空间纯度要求的图像数量多达数百万，研究团队无法从GEE中导出全部此类图像。针对这类类别，研究团队导出了14,000张分层随机抽样样本，并额外提供了一个包含数据集中实际收录图像的CSV文件。换言之，针对这7个LULC类别，本数据集提供两类CSV文件： - 包含该类别所有已导出图像的CSV文件 - 包含该类别所有符合100%空间纯度要求的图像（含已导出与未导出图像）的CSV文件，以供用户按需自行导出。此类CSV文件的文件名以"including_non_downloaded_images"结尾。 本数据集© Sentinel2GlobalLULC由Yassir Benhammou、Domingo Alcaraz-Segura、Emilio Guirado、Rohaifa Khaldi、Boujemâa Achchab、Francisco Herrera与Siham Tabik创建，采用署名4.0国际许可（Attribution 4.0 International, CC-BY 4.0）协议发布。