# reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis

arXiv: https://arxiv.org/abs/2407.03653 · HTML: https://mlsanity.com/paper/2407.03653/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 29 references that are in the map.
- First posted on arXiv 4 Jul 2024.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://mlsanity.com/paper/2301.00808/) (2023)
- [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://mlsanity.com/paper/2110.02178/) (2021)
- [BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understanding](https://mlsanity.com/paper/1902.06148/) (2019)
- [Patches Are All You Need?](https://mlsanity.com/paper/2201.09792/) (2022)
- [MobileNetV4 -- Universal Models for the Mobile Ecosystem](https://mlsanity.com/paper/2404.10518/) (2024)
- [InceptionNeXt: When Inception Meets ConvNeXt](https://mlsanity.com/paper/2303.16900/) (2023)
- [Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forward](https://mlsanity.com/paper/2305.08413/) (2023)
- [DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs](https://mlsanity.com/paper/2403.19588/) (2024)
- [Ben-ge: Extending BigEarthNet with Geographical and Environmental Data](https://mlsanity.com/paper/2307.01741/) (2023)

## Built on by (most cited first, 30 of 48)

- [Towards a Unified Copernicus Foundation Model for Earth Vision](https://mlsanity.com/paper/2503.11849/) (2025)
- [Panopticon: Advancing Any-Sensor Foundation Models for Earth Observation](https://mlsanity.com/paper/2503.10845/) (2025)
- [GEO-Bench-2: From Performance to Capability, Rethinking Evaluation in Geospatial AI](https://mlsanity.com/paper/2511.15658/) (2025)
- [EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation](https://mlsanity.com/paper/2605.29330/) (2026)
- [Fine-tune Smarter, Not Harder: Parameter-Efficient Fine-Tuning for Geospatial Foundation Models](https://mlsanity.com/paper/2504.17397/) (2025)
- [On the Effectiveness of Methods and Metrics for Explainable AI in Remote Sensing Image Scene Classification](https://mlsanity.com/paper/2507.05916/) (2025)
- [CSMoE: An Efficient Remote Sensing Foundation Model with Soft Mixture-of-Experts](https://mlsanity.com/paper/2509.14104/) (2025)
- [Building Age Estimation: A New Multi-Modal Benchmark Dataset and Community Challenge](https://mlsanity.com/paper/2502.13818/) (2025)
- [Checkmate: interpretable and explainable RSVQA is the endgame](https://mlsanity.com/paper/2508.13086/) (2025)
- [SIMPLER: Efficient Foundation Model Adaptation via Similarity-Guided Layer Pruning for Earth Observation](https://mlsanity.com/paper/2603.19873/) (2026)
- [BigEarthNet.txt: A Large-Scale Multi-Sensor Image-Text Dataset and Benchmark for Earth Observation](https://mlsanity.com/paper/2603.29630/) (2026)
- [Agentic AI for Remote Sensing: Technical Challenges and Research Directions](https://mlsanity.com/paper/2604.24919/) (2026)
- [A General Purpose Spectral Foundational Model for Both Proximal and Remote Sensing Spectral Imaging](https://mlsanity.com/paper/2503.01628/) (2025)
- [On the Generalization of Representation Uncertainty in Earth Observation](https://mlsanity.com/paper/2503.07082/) (2025)
- [Atomizer: Generalizing to new modalities by breaking satellite images down to a set of scalars](https://mlsanity.com/paper/2506.13542/) (2025)
- [Video Compression for Spatiotemporal Earth System Data](https://mlsanity.com/paper/2506.19656/) (2025)
- [MoSAiC: Multi-Modal Multi-Label Supervision-Aware Contrastive Learning for Remote Sensing](https://mlsanity.com/paper/2507.08683/) (2025)
- [FedX: Explanation-Guided Pruning for Communication-Efficient Federated Learning in Remote Sensing](https://mlsanity.com/paper/2508.06256/) (2025)
- [UniDiff: Parameter-Efficient Adaptation of Diffusion Models for Land Cover Classification with Multi-Modal Remotely Sensed Imagery and Sparse Annotations](https://mlsanity.com/paper/2512.00261/) (2025)
- [Concatenated Matrix SVD: Compression Bounds, Incremental Approximation, and Error-Constrained Clustering](https://mlsanity.com/paper/2601.11626/) (2026)
- [CBEN -- A Multimodal Machine Learning Dataset for Cloud Robust Remote Sensing Image Understanding](https://mlsanity.com/paper/2602.12652/) (2026)
- [BELDE: Building a Large-scale Earth-observation Land-cover Dataset for Europe](https://mlsanity.com/paper/2606.20909/) (2026)
- [A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features](https://mlsanity.com/paper/2501.10144/) (2025)
- [Communication-Efficient Federated Learning Based on Explanation-Guided Pruning for Remote Sensing Image Classification](https://mlsanity.com/paper/2501.11493/) (2025)
- [ChromaFormer: A Scalable and Accurate Transformer Architecture for Land Cover Classification](https://mlsanity.com/paper/2503.08534/) (2025)
- [Machine Learning Models for Soil Parameter Prediction Based on Satellite, Weather, Clay and Yield Data](https://mlsanity.com/paper/2503.22276/) (2025)
- [Magnifier: A Multi-grained Neural Network-based Architecture for Burned Area Delineation](https://mlsanity.com/paper/2504.19589/) (2025)
- [CLOSP: A Unified Semantic Space for SAR, MSI, and Text in Remote Sensing](https://mlsanity.com/paper/2507.10403/) (2025)
- [Not every day is a sunny day: Synthetic cloud injection for deep land cover segmentation robustness evaluation across data sources](https://mlsanity.com/paper/2510.03006/) (2025)
- [Adaptive Gradient Calibration for Single-Positive Multi-Label Learning in Remote Sensing Image Scene Classification](https://mlsanity.com/paper/2510.08269/) (2025)

## Abstract (arXiv)

This paper presents refined BigEarthNet (reBEN) that is a large-scale, multi-modal remote sensing dataset constructed to support deep learning (DL) studies for remote sensing image analysis. The reBEN dataset consists of 549,488 pairs of Sentinel-1 and Sentinel-2 image patches. To construct reBEN, we initially consider the Sentinel-1 and Sentinel-2 tiles used to construct the BigEarthNet dataset and then divide them into patches of size 1200 m x 1200 m. We apply atmospheric correction to the Sentinel-2 patches using the latest version of the sen2cor tool, resulting in higher-quality patches compared to those present in BigEarthNet. Each patch is then associated with a pixel-level reference map and scene-level multi-labels. This makes reBEN suitable for pixel- and scene-based learning tasks. The labels are derived from the most recent CORINE Land Cover (CLC) map of 2018 by utilizing the 19-class nomenclature as in BigEarthNet. The use of the most recent CLC map results in overcoming the label noise present in BigEarthNet. Furthermore, we introduce a new geographical-based split assignment algorithm that significantly reduces the spatial correlation among the train, validation, and test sets with respect to those present in BigEarthNet. This increases the reliability of the evaluation of DL models. To minimize the DL model training time, we introduce software tools that convert the reBEN dataset into a DL-optimized data format. In our experiments, we show the potential of reBEN for multi-modal multi-label image classification problems by considering several state-of-the-art DL models. The pre-trained model weights, associated code, and complete dataset are available at https://bigearth.net.