reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis
<|reference_start|>reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis: 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. Each patch is then associated with a pixel-level reference map and scene-level multi-labels. This makes reBEN suitable for pixel- and scene-based learning tasks. The labels are derived from the most recent CORINE Land Cover (CLC) map of 2018 by utilizing the 19-class nomenclature as in BigEarthNet. The use of the most recent CLC map results in overcoming the label noise present in BigEarthNet. Furthermore, we introduce a new geographical-based split assignment algorithm that significantly reduces the spatial correlation among the train, validation, and test sets with respect to those present in BigEarthNet. This increases the reliability of the evaluation of DL models. To minimize the DL model training time, we introduce software tools that convert the reBEN dataset into a DL-optimized data format. In our experiments, we show the potential of reBEN for multi-modal multi-label image classification problems by considering several state-of-the-art DL models. The pre-trained model weights, associated code, and complete dataset are available at https://bigearth.net.<|reference_end|>
@article{clasen2024reben:,
    title={reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis},
    author={Kai Norman Clasen, Leonard Hackel, Tom Burgert, Gencer Sumbul, Beg"um
  Demir, Volker Markl},
    journal={arXiv preprint arXiv:2407.03653},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.03653},
    primaryClass={cs.CV eess.IV}
}