Ben-ge: Extending BigEarthNet with Geographical and Environmental Data
This provides a new test bed for Earth observation applications, though it is incremental as it builds on an existing dataset.
The authors tackled the problem of limited data modalities in Earth observation analysis by creating the ben-ge dataset, which extends BigEarthNet-MM with freely available geographical and environmental data, and demonstrated its value for land-use/land-cover classification and segmentation tasks.
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.