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Commit 3e50aaf1 authored by tristan.kreuziger's avatar tristan.kreuziger

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# Deep Learning Models for BigEarthNet-S2 with 19 Classes
This repository contains code to use the [BigEarthNet](http://bigearth.net) Sentinel-2 (denoted as BigEarthNet-S2) 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 the creation of the nomenclature of 19 classes is currently under review and will be referenced here in the future. If you are interested in BigEarthNet-S2 with the original CLC Level-3 class nomenclature of 43 classes, please check [here](https://gitlab.tu-berlin.de/rsim/bigearthnet-models/tree/master).
A paper describing the creation of the nomenclature of 19 classes is currently under review and will be referenced here in the future. If you are interested in BigEarthNet-S2 with the original CLC Level-3 class nomenclature of 43 classes, please check [here](https://git.tu-berlin.de/rsim/BigEarthNet-S2_43-classes_models).
## Pre-trained Deep Learning Models
We provide code and model weights for the following deep learning models that have been pre-trained on BigEarthNet-S2 with the nomenclature of 19 classes for scene classification:
......@@ -16,7 +16,7 @@ We provide code and model weights for the following deep learning models that ha
| ResNet101 | [ResNet101.zip](http://bigearth.net/static/pretrained-models/BigEarthNet-S2_19-Classes/ResNet101.zip) |
| ResNet152 | [ResNet152.zip](http://bigearth.net/static/pretrained-models/BigEarthNet-S2_19-Classes/ResNet152.zip) |
The TensorFlow code for these models can be found [here](https://gitlab.tu-berlin.de/rsim/bigearthnet-models-tf).
The TensorFlow code for these models can be found [here](https://git.tu-berlin.de/rsim/bigearthnet-models-tf).
The pre-trained models associated to other deep learning libraries will be released soon.
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