Sentinel-2 Sample Data

This repository contains 5 Sentinel-2 Level-2A (12 bands) images that are part of the BigEarthNet Dataset (Sumbul et al. 2019, https://bigearth.net/). The images in this dataset focus on coastal areas. The image data for each scene and band were upscaled to a common ground sample distance of 10m per pixel using linear interpolation. Furthermore, all bands of each scence were combined into a single NumPy array and stored into separate .npy binary files. Data processing was performed by Linus Scheibenreif, University of St. Gallen. The data can be easily read in with Python using the following code snippet: import osimport numpy as np data = []for filename in os.listdir('data/'): if filename.endswith('.npy'): data.append(np.load(open(os.path.join('data', filename), 'rb'), allow_pickle=True))data = np.array(data) This repository also contains the file coastal_labels.json, which contains polygons for labels grassland, forest, water and sand, using the YOLO format. This dataset is provided mainly for teaching purposes under the Creative Commons Attribution 4.0 International licence. BigEarthNet data are provided under the Community Data License Agreement (Permissive, Version 1.0). Michael Mommert, Stuttgart University of Applied Sciences, 2025-03-07本仓库包含5张哨兵二号（Sentinel-2）Level-2A（12波段）影像，属于BigEarthNet数据集（Sumbul等，2019，https://bigearth.net/）的组成部分，该数据集的影像聚焦于沿海区域。 针对每个场景与波段的影像数据，已通过线性插值法将其统一放大至10米每像素的地面采样距离。此外，每个场景的所有波段已被合并为单个NumPy数组，并存储为独立的.npy二进制文件。数据处理工作由圣加仑大学的Linus Scheibenreif完成。 可通过如下Python代码片段轻松读取该数据： import osimport numpy as np data = []for filename in os.listdir('data/'): if filename.endswith('.npy'): data.append(np.load(open(os.path.join('data', filename), 'rb'), allow_pickle=True))data = np.array(data) 本仓库还包含coastal_labels.json文件，该文件以YOLO格式存储了草地、森林、水体与沙地的标注多边形。 本数据集主要以教学为目的，采用知识共享署名4.0国际许可协议（Creative Commons Attribution 4.0 International licence）进行发布。BigEarthNet数据则采用社区数据许可协议（宽松版，1.0版）进行分发。 迈克尔·莫默特（Michael Mommert），斯图加特应用技术大学，2025年3月7日