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arXiv · 2307.01741

Ben-ge: Extending BigEarthNet with Geographical and Environmental Data

Michael Mommert·Nicolas Kesseli·Joëlle Hanna·Linus Scheibenreif·Damian Borth·Begüm Demir
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Abstract

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.

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Michael Mommert, Nicolas Kesseli, Joëlle Hanna, Linus Scheibenreif, Damian Borth, Begüm Demir. 2023-07-04. Ben-ge: Extending BigEarthNet with Geographical and Environmental Data. https://arxiv.org/abs/2307.01741

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