# BigEarthNet Dataset with A New Class-Nomenclature for Remote Sensing Image Understanding

arXiv: https://arxiv.org/abs/2001.06372 · HTML: https://mlsanity.com/paper/2001.06372/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2020.

## Built on by (most cited first, 14 of 14)

- [Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data](https://mlsanity.com/paper/2103.16607/) (2021)
- [Self-Supervised Learning of Remote Sensing Scene Representations Using Contrastive Multiview Coding](https://mlsanity.com/paper/2104.07070/) (2021)
- [EarthNets: Empowering AI in Earth Observation](https://mlsanity.com/paper/2210.04936/) (2022)
- [Evaluating explainable artificial intelligence methods for multi-label deep learning classification tasks in remote sensing](https://mlsanity.com/paper/2104.01375/) (2021)
- [Extending global-local view alignment for self-supervised learning with remote sensing imagery](https://mlsanity.com/paper/2303.06670/) (2023)
- [On the Effects of Different Types of Label Noise in Multi-Label Remote Sensing Image Classification](https://mlsanity.com/paper/2207.13975/) (2022)
- [Mind the Modality Gap: Towards a Remote Sensing Vision-Language Model via Cross-modal Alignment](https://mlsanity.com/paper/2402.09816/) (2024)
- [Benchmarking and scaling of deep learning models for land cover image classification](https://mlsanity.com/paper/2111.09451/) (2021)
- [JUWELS Booster -- A Supercomputer for Large-Scale AI Research](https://mlsanity.com/paper/2108.11976/) (2021)
- [SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting](https://mlsanity.com/paper/2412.08536/) (2024)
- [Efficient Self-Supervised Learning for Earth Observation via Dynamic Dataset Curation](https://mlsanity.com/paper/2504.06962/) (2025)
- [OpenEarthSensing: Large-Scale Fine-Grained Benchmark for Open-World Remote Sensing](https://mlsanity.com/paper/2502.20668/) (2025)
- [Land Cover and Land Use Detection using Semi-Supervised Learning](https://mlsanity.com/paper/2212.11211/) (2022)
- [Multi-Label Scene Classification in Remote Sensing Benefits from Image Super-Resolution](https://mlsanity.com/paper/2501.06720/) (2025)

## Abstract (arXiv)

This paper presents BigEarthNet that is a large-scale Sentinel-2 multispectral image dataset with a new class nomenclature to advance deep learning (DL) studies in remote sensing (RS). BigEarthNet is made up of 590,326 image patches annotated with multi-labels provided by the CORINE Land Cover (CLC) map of 2018 based on its most thematic detailed Level-3 class nomenclature. Initial research demonstrates that some CLC classes are challenging to be accurately described by considering only Sentinel-2 images. To increase the effectiveness of BigEarthNet, in this paper we introduce an alternative class-nomenclature to allow DL models for better learning and describing the complex spatial and spectral information content of the Sentinel-2 images. This is achieved by interpreting and arranging the CLC Level-3 nomenclature based on the properties of Sentinel-2 images in a new nomenclature of 19 classes. Then, the new class-nomenclature of BigEarthNet is used within state-of-the-art DL models in the context of multi-label classification. Results show that the models trained from scratch on BigEarthNet outperform those pre-trained on ImageNet, especially in relation to some complex classes including agriculture, other vegetated and natural environments. All DL models are made publicly available at http://bigearth.net/#downloads, offering an important resource to guide future progress on RS image analysis.