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Deep learning methods have proven to be a powerful tool in the analysis oflarge amounts of complex Earth observation data. However, while Earthobservation data are multi-modal in most cases, only single or few modalitiesare typically considered. In this work, we present the ben-ge dataset, whichsupplements the BigEarthNet-MM dataset by compiling freely and globallyavailable geographical and environmental data. Based on this dataset, weshowcase the value of combining different data modalities for the downstreamtasks of patch-based land-use/land-cover classification and land-use/land-coversegmentation. ben-ge is freely available and expected to serve as a test bedfor fully supervised and self-supervised Earth observation applications.

Ben-ge: Extending BigEarthNet with Geographical and Environmental Data