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# Deep Learning Models for BigEarthNet-MM with 19 Classes
This repository contains code to use the [multi-modal BigEarthNet (BigEarthNet-MM)](http://bigearth.net) archive with the nomenclature of 19 classes for deep learning applications. The nomenclature of 19 classes was defined by interpreting and arranging the CORINE Land Cover (CLC) Level-3 nomenclature based on the properties of Sentinel-2 images. This class nomenclature is the product of a collaboration between the [Direção-Geral do Território](http://www.dgterritorio.pt/) in Lisbon, Portugal and the [Remote Sensing Image Analysis (RSiM)](https://www.rsim.tu-berlin.de/) group at TU Berlin, Germany.
A paper describing BigEarthNet-MM and the creation of the nomenclature of 19 classes is currently under review and will be referenced here in the future.
If you use the BigEarthNet-MM archive or our pre-trained models, please cite the paper given below:
> G. Sumbul, A. d. Wall, T. Kreuziger, F. Marcelino, H. Costa, P. Benevides, M. Caetano, B. Demir, V. Markl, “[BigEarthNet-MM: A Large Scale Multi-Modal Multi-Label Benchmark Archive for Remote Sensing Image Classification and Retrieval](https://arxiv.org/abs/2105.07921)”, arXiv:2105.07921, 2021.
```
@misc{BigEarthNet-MM,
title={BigEarthNet-MM: A Large Scale Multi-Modal Multi-Label Benchmark Archive for Remote Sensing Image Classification and Retrieval},
author={Gencer Sumbul and Arne de Wall and Tristan Kreuziger and Filipe Marcelino and Hugo Costa and Pedro Benevides and Mário Caetano and Begüm Demir and Volker Markl},
year={2021},
eprint={2105.07921},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
## Pre-trained Deep Learning Models
We provide code and model weights for the following deep learning models that have been pre-trained on BigEarthNet-MM with the nomenclature of 19 classes for scene classification:
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