Ben-ge: Extending BigEarthNet with Geographical and Environmental Data

• URL: http://arxiv.org/abs/2307.01741v1
• Date: Tue, 4 Jul 2023 14:17:54 GMT
• Title: Ben-ge: Extending BigEarthNet with Geographical and Environmental Data
• Authors: Michael Mommert, Nicolas Kesseli, Jo\"elle Hanna, Linus Scheibenreif, Damian Borth, Beg\"um Demir
• Abstract summary: 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.
• Score: 1.1377027568901037
• License: http://creativecommons.org/licenses/by/4.0/
• 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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