BigEarthNet

The BigEarthNet archive was constructed by the Remote Sensing Image Analysis (RSiM) Group and the Database Systems and Information Management (DIMA) Group at the Technische Universit?t Berlin (TU Berlin). This work is supported by the European Research Council under the ERC Starting Grant BigEarth and by the German Ministry for Education and Research as Berlin Big Data Center (BBDC). BigEarthNet is a new large-scale Sentinel-2 benchmark archive, consisting of 590,326 Sentinel-2 image patches. To construct BigEarthNet, 125 Sentinel-2 tiles acquired between June 2017 and May 2018 over the 10 countries (Austria, Belgium, Finland, Ireland, Kosovo, Lithuania, Luxembourg, Portugal, Serbia, Switzerland) of Europe were initially selected. All the tiles were atmospherically corrected by the Sentinel-2 Level 2A product generation and formatting tool (sen2cor). Then, they were divided into 590,326 non-overlapping image patches. Each image patch was annotated by the multiple land-cover classes (i.e., multi-labels) that were provided from the CORINE Land Cover database of the year 2018 (CLC 2018). BigEarthNet is significantly larger than the existing archives in remote sensing and opens up promising directions to advance research for the analysis of large-scale remote sensing image archives. It is also very convenient to be used as a training source in the context of deep learning for knowledge discovery from big archives in remote sensing.

BigEarthNet数据集档案由柏林工业大学（Technische Universität Berlin，TU Berlin）的遥感图像分析（Remote Sensing Image Analysis，RSiM）小组与数据库系统与信息管理（Database Systems and Information Management，DIMA）小组构建。本项目得到欧洲研究理事会（European Research Council）ERC启动基金BigEarth以及德国联邦教育与研究部资助的柏林大数据中心（Berlin Big Data Center，BBDC）支持。BigEarthNet是一款全新的大规模哨兵二号（Sentinel-2）基准数据集档案，包含590326幅哨兵二号图像块。为构建该数据集，研究团队最初选取了2017年6月至2018年5月间覆盖欧洲10个国家（奥地利、比利时、芬兰、爱尔兰、科索沃、立陶宛、卢森堡、葡萄牙、塞尔维亚、瑞士）的125幅哨兵二号影像瓦片；所有瓦片均通过哨兵二号Level 2A产品生成与格式化工具（sen2cor）完成大气校正，随后被切分为590326个互不重叠的图像块。每个图像块均基于2018年CORINE土地覆盖数据库（CORINE Land Cover 2018，CLC 2018）提供的多土地覆盖类别（即多标签）进行标注。BigEarthNet的规模远超现有遥感数据集档案，为大规模遥感影像档案分析的研究进展开辟了极具潜力的方向，同时也非常适合作为深度学习场景下的训练数据源，用于从大规模遥感档案中挖掘知识。

搜集汇总

背景与挑战

背景概述

BigEarthNet是一个大规模Sentinel-2卫星图像数据集，包含590,326个图像块，覆盖欧洲10个国家，每个图像块标注了多重土地覆盖类别，适用于遥感图像分析和深度学习研究。