mlsanityData to 7 Oct 2026

reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis

arXiv 2407.03653 ↗First posted 4 Jul 2024cs.CV eess.IVcited by 48

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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.

Key facts. Cited by 48 papers in the map (data through 7 Oct 2026). Builds on 12 of its 29 references that are in the map. First posted on arXiv 4 Jul 2024.

Builds on

12 of 29 references.

References · 12

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2021MLP-Mixer: An all-MLP Architecture for Visioncited by 1,441
  4. 2023ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoderscited by 703
  5. 2021MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformercited by 636
  6. 2019BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understandingcited by 231
  7. 2022Patches Are All You Need?cited by 201
  8. 2024MobileNetV4 -- Universal Models for the Mobile Ecosystemcited by 98
  9. 2023InceptionNeXt: When Inception Meets ConvNeXtcited by 84
  10. 2023Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forwardcited by 28
  11. 2024DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTscited by 12
  12. 2023Ben-ge: Extending BigEarthNet with Geographical and Environmental Datacited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 26; 2026: 22.

48 papers.

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  1. 2025Towards a Unified Copernicus Foundation Model for Earth Visioncited by 52
  2. 2025Panopticon: Advancing Any-Sensor Foundation Models for Earth Observationcited by 34
  3. 2025GEO-Bench-2: From Performance to Capability, Rethinking Evaluation in Geospatial AIcited by 19
  4. 2025Fine-tune Smarter, Not Harder: Parameter-Efficient Fine-Tuning for Geospatial Foundation Modelscited by 9
  5. 2026EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observationcited by 8
  6. 2025On the Effectiveness of Methods and Metrics for Explainable AI in Remote Sensing Image Scene Classificationcited by 3
  7. 2025CSMoE: An Efficient Remote Sensing Foundation Model with Soft Mixture-of-Expertscited by 3
  8. 2025Building Age Estimation: A New Multi-Modal Benchmark Dataset and Community Challengecited by 2
  9. 2025Checkmate: interpretable and explainable RSVQA is the endgamecited by 2
  10. 2026BigEarthNet.txt: A Large-Scale Multi-Sensor Image-Text Dataset and Benchmark for Earth Observationcited by 2
  11. 2025A General Purpose Spectral Foundational Model for Both Proximal and Remote Sensing Spectral Imagingcited by 1
  12. 2025On the Generalization of Representation Uncertainty in Earth Observationcited by 1
36 more
  1. 2025Atomizer: Generalizing to new modalities by breaking satellite images down to a set of scalarscited by 1
  2. 2025Video Compression for Spatiotemporal Earth System Datacited by 1
  3. 2025FedX: Explanation-Guided Pruning for Communication-Efficient Federated Learning in Remote Sensingcited by 1
  4. 2026Concatenated Matrix SVD: Compression Bounds, Incremental Approximation, and Error-Constrained Clusteringcited by 1
  5. 2026CBEN -- A Multimodal Machine Learning Dataset for Cloud Robust Remote Sensing Image Understandingcited by 1
  6. 2026SIMPLER: Efficient Foundation Model Adaptation via Similarity-Guided Layer Pruning for Earth Observationcited by 1
  7. 2026Agentic AI for Remote Sensing: Technical Challenges and Research Directionscited by 1
  8. 2026BELDE: Building a Large-scale Earth-observation Land-cover Dataset for Europecited by 1
  9. 2025A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Featurescited by 0
  10. 2025Communication-Efficient Federated Learning Based on Explanation-Guided Pruning for Remote Sensing Image Classificationcited by 0
  11. 2025ChromaFormer: A Scalable and Accurate Transformer Architecture for Land Cover Classificationcited by 0
  12. 2025Machine Learning Models for Soil Parameter Prediction Based on Satellite, Weather, Clay and Yield Datacited by 0
  13. 2025Magnifier: A Multi-grained Neural Network-based Architecture for Burned Area Delineationcited by 0
  14. 2025MoSAiC: Multi-Modal Multi-Label Supervision-Aware Contrastive Learning for Remote Sensingcited by 0
  15. 2025CLOSP: A Unified Semantic Space for SAR, MSI, and Text in Remote Sensingcited by 0
  16. 2025Not every day is a sunny day: Synthetic cloud injection for deep land cover segmentation robustness evaluation across data sourcescited by 0
  17. 2025Adaptive Gradient Calibration for Single-Positive Multi-Label Learning in Remote Sensing Image Scene Classificationcited by 0
  18. 2025WaveMAE: Wavelet decomposition Masked Auto-Encoder for Remote Sensingcited by 0
  19. 2025EoS-FM: Can an Ensemble of Specialist Models act as a Generalist Feature Extractor?cited by 0
  20. 2025UniDiff: Parameter-Efficient Adaptation of Diffusion Models for Land Cover Classification with Multi-Modal Remotely Sensed Imagery and Sparse Annotationscited by 0
  21. 2025DisentangleFormer: Spatial-Channel Decoupling for Multi-Channel Visioncited by 0
  22. 2026Rank-based Geographical Regularization: Revisiting Contrastive Self-Supervised Learning for Multispectral Remote Sensing Imagerycited by 0
  23. 2026OSCAR: Optical-aware Semantic Control for Aleatoric Refinement in Sar-to-Optical Translationcited by 0
  24. 2026Noise-Adaptive Regularization for Robust Multi-Label Remote Sensing Image Classificationcited by 0
  25. 2026Hide and Seek: Investigating Redundancy in Earth Observation Imagerycited by 0
  26. 2026Sentinel2Cap: A Human-Annotated Benchmark Dataset for Multimodal Remote Sensing Image Captioningcited by 0
  27. 2026Developing a foundation model for high-resolution remote sensing data of the Netherlandscited by 0
  28. 2026OSMGraphCLIP: Learning Global Location Representations from OpenStreetMap Graphscited by 0
  29. 2026Changing Modalities: Adapting Remote Sensing Models to New Satellites and Sensorscited by 0
  30. 2026Topology-Informed Neural Networks for Flood Detection in Optical and Synthetic Aperture Radar Imagerycited by 0
  31. 2026On the Effectiveness of Adaptation Strategies for VLM-Based Federated Learning in Remote Sensingcited by 0
  32. 2026Far from the Crowd: Scalable Self-Supervised Learning via Geographic Isolationcited by 0
  33. 2026MEOX: Compact Multimodal Mixture-of-Experts for Earth Observationcited by 0
  34. 2026GeoNLI - A Natural Language Interpreter for Satellite Imagerycited by 0
  35. 2026Planetary Feature Fields are Scalable Earth Representationscited by 0
  36. 2026FedMAD: Modulation-Aware Directional Aggregation for Federated Learning in Remote Sensing Image Classificationcited by 0