  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.
