Deep learning methods have proven to be a powerful tool in the analysis of large amounts of complex Earth observation data. However, while Earth observation data are multi-modal in most cases, only single or few modalities are typically considered. In this work, we present the ben-ge dataset, which supplements the BigEarthNet-MM dataset by compiling freely and globally available geographical and environmental data. Based on this dataset, we showcase the value of combining different data modalities for the downstream tasks of patch-based land-use/land-cover classification and land-use/land-cover segmentation. ben-ge is freely available and expected to serve as a test bed for fully supervised and self-supervised Earth observation applications.
@article{arxiv.2307.01741,
title = {Ben-ge: Extending BigEarthNet with Geographical and Environmental Data},
author = {Michael Mommert and Nicolas Kesseli and Joëlle Hanna and Linus Scheibenreif and Damian Borth and Begüm Demir},
journal= {arXiv preprint arXiv:2307.01741},
year = {2023}
}
Comments
Accepted for presentation at the IEEE International Geoscience and Remote Sensing Symposium 2023