BigEarthNet v2.0

The BigEarthNet v2.0 dataset was constructed by the Remote Sensing Image Analysis (RSiM) Group and the Database Systems and Information Management (DIMA) Group at the Technische Universität Berlin (TU Berlin). This work is supported by the European Research Council under the ERC Starting Grant BigEarth and by the Berlin Institute for the Foundations of Learning and Data (BIFOLD).

BigEarthNet v2.0 is a benchmark dataset consisting of 549,488 pairs of Sentinel-1 and Sentinel-2 image patches. To construct BigEarthNet v2.0 with Sentinel-2 image patches (called as BigEarthNet-S2), 115 Sentinel-2 tiles acquired between June 2017 and May 2018 over 10 countries (Austria, Belgium, Finland, Ireland, Kosovo, Lithuania, Luxembourg, Portugal, Serbia, and Switzerland) of Europe were initially selected. All the tiles were atmospherically corrected by the Sentinel-2 Level 2A product generation and formatting tool (sen2cor v2.11). Then, they were divided into 549,488 image patches. Each image patch was associated with a pixel-level reference map and multiple land-cover class labels (i.e., multi-labels) that were derived from the most recent CORINE Land Cover database of the year 2018 (CLC2018 v2020_u1).

To construct BigEarthNet v2.0 with Sentinel-1 image patches (called as BigEarthNet-S1), 312 Sentinel-1 scenes acquired between June 2017 and May 2018 that jointly cover the area of all original 115 Sentinel-2 tiles with close temporal proximity were selected and processed. BigEarthNet-S1 consists of 549,488 preprocessed Sentinel-1 image patches – one for each Sentinel-2 patch.

The BigEarthNet v2.0 dataset includes several significant improvements compared to the previous 1.0 version. These changes include the application of the latest atmospheric correction tool (sen2cor), which results in higher-quality patches. Additionally, the most recent version of the CLC2018 database was utilized to extract label information, overcoming label noise present in BigEarthNet v1.0. Apart from providing patch-level labels, v2.0 additionally includes pixel-level reference maps, making the dataset suitable for pixel- and scene-based learning tasks. Furthermore, BigEarthNet v2.0 introduces a new geographical-based split assignment algorithm, which significantly reduces spatial correlation among the train, validation, and test sets compared to v1.0.

If you use this work, please cite:

K. Clasen, L. Hackel, T. Burgert, G. Sumbul, B. Demir, V. Markl, “reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis”, IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2025.