mlsanityData to 14 Oct 2026

BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understanding

arXiv 1902.06148 ↗First posted 16 Feb 2019cs.CVcited by 234

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Abstract (arXiv)

This paper presents the BigEarthNet that is a new large-scale multi-label Sentinel-2 benchmark archive. The BigEarthNet consists of 590,326 Sentinel-2 image patches, each of which is a section of i) 120x120 pixels for 10m bands; ii) 60x60 pixels for 20m bands; and iii) 20x20 pixels for 60m bands. Unlike most of the existing archives, each image patch is annotated by multiple land-cover classes (i.e., multi-labels) that are provided from the CORINE Land Cover database of the year 2018 (CLC 2018). The BigEarthNet is significantly larger than the existing archives in remote sensing (RS) and thus is much more convenient to be used as a training source in the context of deep learning. This paper first addresses the limitations of the existing archives and then describes the properties of the BigEarthNet. Experimental results obtained in the framework of RS image scene classification problems show that a shallow Convolutional Neural Network (CNN) architecture trained on the BigEarthNet provides much higher accuracy compared to a state-of-the-art CNN model pre-trained on the ImageNet (which is a very popular large-scale benchmark archive in computer vision). The BigEarthNet opens up promising directions to advance operational RS applications and research in massive Sentinel-2 image archives.

Key facts. Cited by 234 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Feb 2019.

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Citing papers per year: 2021: 26; 2022: 27; 2023: 31; 2024: 44; 2025: 65; 2026: 41.

234 papers.

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  1. 2022SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagerycited by 408
  2. 2023SpectralGPT: Spectral Remote Sensing Foundation Modelcited by 252
  3. 2021Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Datacited by 215
  4. 2023SkySense: A Multi-Modal Remote Sensing Foundation Model Towards Universal Interpretation for Earth Observation Imagerycited by 205
  5. 2022SatlasPretrain: A Large-Scale Dataset for Remote Sensing Image Understandingcited by 167
  6. 2023RS5M and GeoRSCLIP: A Large Scale Vision-Language Dataset and A Large Vision-Language Model for Remote Sensingcited by 151
  7. 2022Self-supervised Learning in Remote Sensing: A Reviewcited by 137
  8. 2023SkyScript: A Large and Semantically Diverse Vision-Language Dataset for Remote Sensingcited by 125
  9. 2023Towards Geospatial Foundation Models via Continual Pretrainingcited by 109
  10. 2022An Empirical Study of Remote Sensing Pretrainingcited by 105
  11. 2022Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIPcited by 97
  12. 2021BigEarthNet-MM: A Large Scale Multi-Modal Multi-Label Benchmark Archive for Remote Sensing Image Classification and Retrievalcited by 92
38 more
  1. 2024EarthDial: Turning Multi-sensory Earth Observations to Interactive Dialoguescited by 85
  2. 2022SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observationcited by 84
  3. 2024Rethinking Transformers Pre-training for Multi-Spectral Satellite Imagerycited by 83
  4. 2023Diversity is Definitely Needed: Improving Model-Agnostic Zero-shot Classification via Stable Diffusioncited by 81
  5. 2024Mission Critical -- Satellite Data is a Distinct Modality in Machine Learningcited by 77
  6. 2024MMEarth: Exploring Multi-Modal Pretext Tasks For Geospatial Representation Learningcited by 77
  7. 2021TorchGeo: Deep Learning With Geospatial Datacited by 73
  8. 2024HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Modelcited by 73
  9. 2023Remote Sensing Vision-Language Foundation Models without Annotations via Ground Remote Alignmentcited by 71
  10. 2023CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representationscited by 69
  11. 2024PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Modelscited by 68
  12. 2024MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretrainingcited by 66
  13. 2023RRSIS: Referring Remote Sensing Image Segmentationcited by 59
  14. 2021Self-Supervised Learning of Remote Sensing Scene Representations Using Contrastive Multiview Codingcited by 58
  15. 2021SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learningcited by 54
  16. 2021Self-Supervised Material and Texture Representation Learning for Remote Sensing Taskscited by 49
  17. 2024reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysiscited by 48
  18. 2024Bridging Remote Sensors with Multisensor Geospatial Foundation Modelscited by 46
  19. 2025Galileo: Learning Global & Local Features of Many Remote Sensing Modalitiescited by 43
  20. 2024SpectralEarth: Training Hyperspectral Foundation Models at Scalecited by 42
  21. 2022EarthNets: Empowering AI in Earth Observationcited by 40
  22. 2024Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation datacited by 39
  23. 2021EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction taskcited by 34
  24. 2021On Circuit-based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classificationcited by 33
  25. 2024On the Foundations of Earth and Climate Foundation Modelscited by 33
  26. 2024Major TOM: Expandable Datasets for Earth Observationcited by 32
  27. 2024Img2Loc: Revisiting Image Geolocalization using Multi-modality Foundation Models and Image-based Retrieval-Augmented Generationcited by 32
  28. 2024GeoLLM-Engine: A Realistic Environment for Building Geospatial Copilotscited by 30
  29. 2023Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forwardcited by 28
  30. 2023USat: A Unified Self-Supervised Encoder for Multi-Sensor Satellite Imagerycited by 28
  31. 2024Multi-Label Guided Soft Contrastive Learning for Efficient Earth Observation Pretrainingcited by 28
  32. 2023Revisiting pre-trained remote sensing model benchmarks: resizing and normalization matterscited by 27
  33. 2021Evaluating explainable artificial intelligence methods for multi-label deep learning classification tasks in remote sensingcited by 26
  34. 2025SkySense V2: A Unified Foundation Model for Multi-modal Remote Sensingcited by 26
  35. 2022Self-supervised remote sensing feature learning: Learning Paradigms, Challenges, and Future Workscited by 24
  36. 2024Specialized Foundation Models Struggle to Beat Supervised Baselinescited by 24
  37. 2025OlmoEarth: Stable Latent Image Modeling for Multimodal Earth Observationcited by 24
  38. 2022Continual Barlow Twins: continual self-supervised learning for remote sensing semantic segmentationcited by 22

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