reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis

Understand

This paper presents refined BigEarthNet (reBEN) that is a large-scale, multi-modal remote sensing dataset constructed to support deep learning (DL) studies for remote sensing image analysis.

• The reBEN dataset consists of 549,488 pairs of Sentinel-1 and Sentinel-2 image patches.
• To construct reBEN, we initially consider the Sentinel-1 and Sentinel-2 tiles used to construct the BigEarthNet dataset and then divide them into patches of size 1200 m x 1200 m.
• We apply atmospheric correction to the Sentinel-2 patches using the latest version of the sen2cor tool, resulting in higher-quality patches compared to those present in BigEarthNet.