# Ben-ge: Extending BigEarthNet with Geographical and Environmental Data

arXiv: https://arxiv.org/abs/2307.01741 · HTML: https://mlsanity.com/paper/2307.01741/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 9 references that are in the map.
- First posted on arXiv 4 Jul 2023.

## Builds on (most cited first, 4 of 4)

- [BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understanding](https://mlsanity.com/paper/1902.06148/) (2019)
- [SEN12MS -- A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusion](https://mlsanity.com/paper/1906.07789/) (2019)
- [BigEarthNet-MM: A Large Scale Multi-Modal Multi-Label Benchmark Archive for Remote Sensing Image Classification and Retrieval](https://mlsanity.com/paper/2105.07921/) (2021)
- [SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation](https://mlsanity.com/paper/2211.07044/) (2022)

## Built on by (most cited first, 5 of 5)

- [MMEarth: Exploring Multi-Modal Pretext Tasks For Geospatial Representation Learning](https://mlsanity.com/paper/2405.02771/) (2024)
- [reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis](https://mlsanity.com/paper/2407.03653/) (2024)
- [Fine-tune Smarter, Not Harder: Parameter-Efficient Fine-Tuning for Geospatial Foundation Models](https://mlsanity.com/paper/2504.17397/) (2025)
- [MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models](https://mlsanity.com/paper/2507.07527/) (2025)
- [Can Multitask Learning Enhance Model Explainability?](https://mlsanity.com/paper/2508.06966/) (2025)

## Abstract (arXiv)

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.