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# BigEarthNet-19 Deep Learning Models # Deep Learning Models for BigEarthNet-S2 with 19 Classes
This repository contains code to use the [BigEarthNet](http://bigearth.net) archive with a new class nomenclature (BigEarthNet-19) for deep learning applications. The new class nomenclature was defined by interpreting and arranging the CORINE Land Cover (CLC) Level-3 nomenclature based on the properties of Sentinel-2 images. The new 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. 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 and evaluation of BigEarthNet-19 is currently under review and will be referenced here in the future. If you are interested in BigEarthNet with the original CLC Level-3 class nomenclature, 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://gitlab.tu-berlin.de/rsim/bigearthnet-models/tree/master).
# Pre-trained Deep Learning Models on BigEarthNet-19 ## Pre-trained Deep Learning Models
We provide code and model weights for the following deep learning models that have been pre-trained on BigEarthNet with the new class nomenclature (BigEarthNet-19) for scene classification: 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:
| Model Names | Pre-Trained TensorFlow Models | | Model Names | Pre-Trained TensorFlow Models |
| ------------ | ------------------------------------------------------------ | | ------------ | ------------------------------------------------------------ |
| K-Branch CNN | [K-BranchCNN.zip](http://bigearth.net/static/pretrained-models/BigEarthNet-19_labels/K-BranchCNN.zip) | | K-Branch CNN | [K-BranchCNN.zip](http://bigearth.net/static/pretrained-models/BigEarthNet-S2_19-Classes/K-BranchCNN.zip) |
| VGG16 | [VGG16.zip](http://bigearth.net/static/pretrained-models/BigEarthNet-19_labels/VGG16.zip) | | VGG16 | [VGG16.zip](http://bigearth.net/static/pretrained-models/BigEarthNet-S2_19-Classes/VGG16.zip) |
| VGG19 | [VGG19.zip](http://bigearth.net/static/pretrained-models/BigEarthNet-19_labels/VGG19.zip) | | VGG19 | [VGG19.zip](http://bigearth.net/static/pretrained-models/BigEarthNet-S2_19-Classes/VGG19.zip) |
| ResNet50 | [ResNet50.zip](http://bigearth.net/static/pretrained-models/BigEarthNet-19_labels/ResNet50.zip) | | ResNet50 | [ResNet50.zip](http://bigearth.net/static/pretrained-models/BigEarthNet-S2_19-Classes/ResNet50.zip) |
| ResNet101 | [ResNet101.zip](http://bigearth.net/static/pretrained-models/BigEarthNet-19_labels/ResNet101.zip) | | ResNet101 | [ResNet101.zip](http://bigearth.net/static/pretrained-models/BigEarthNet-S2_19-ClassesResNet101.zip) |
| ResNet152 | [ResNet152.zip](http://bigearth.net/static/pretrained-models/BigEarthNet-19_labels/ResNet152.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://gitlab.tu-berlin.de/rsim/bigearthnet-models-tf).
The pre-trained models associated to other deep learning libraries will be released soon. The pre-trained models associated to other deep learning libraries will be released soon.
# Generation of Training/Test/Validation Splits ## Generation of Training/Test/Validation Splits
After downloading the raw images from http://bigearth.net, they need to be prepared for your ML application. We provide the script `prep_splits_BigEarthNet-19.py` for this purpose. It generates consumable data files (i.e., TFRecord) for training, validation and test splits which are suitable to use with TensorFlow. Suggested splits can be found with corresponding csv files under `splits` folder. The following command line arguments for `prep_splits_BigEarthNet-19.py` can be specified: After downloading the raw images from http://bigearth.net, they need to be prepared for your ML application. We provide the script `prep_splits_19_classes.py` for this purpose. It generates consumable data files (i.e., TFRecord) for training, validation and test splits which are suitable to use with TensorFlow. Suggested splits can be found with corresponding csv files under `splits` folder. The following command line arguments for `prep_splits_19_classes.py` can be specified:
* `-r` or `--root_folder`: The root folder containing the raw images you have previously downloaded. * `-r` or `--root_folder`: The root folder containing the raw images you have previously downloaded.
* `-o` or `--out_folder`: The output folder where the resulting files will be created. * `-o` or `--out_folder`: The output folder where the resulting files will be created.
* `-n` or `--splits`: A list of CSV files each of which contains the patch names of corresponding split. * `-n` or `--splits`: A list of CSV files each of which contains the patch names of corresponding split.
* `-l` or `--library`: A flag to indicate for which ML library data files will be prepared: TensorFlow. * `-l` or `--library`: A flag to indicate for which ML library data files will be prepared: TensorFlow.
* `--update_json`: A flag to indicate that this script will also change the original json files of the BigEarthNet by updating labels * `--update_json`: A flag to indicate that this script will also change the original json files of the BigEarthNet-S2 by updating labels
To run the script, either the GDAL or the rasterio package should be installed. The TensorFlow package should also be installed. The script is tested with Python 2.7, TensorFlow 1.3, and Ubuntu 16.04. To run the script, either the GDAL or the rasterio package should be installed. The TensorFlow package should also be installed. The script is tested with Python 2.7, TensorFlow 1.3, and Ubuntu 16.04.
**Note**: BigEarthNet patches with high density snow, cloud and cloud shadow are not included in the training, test and validation sets constructed by the provided scripts (see the list of patches with seasonal snow [here](http://bigearth.net/static/documents/patches_with_seasonal_snow.csv) and that of cloud and cloud shadow [here](http://bigearth.net/static/documents/patches_with_cloud_and_shadow.csv)). **Note**: BigEarthNet-S2 patches with high density snow, cloud and cloud shadow are not included in the training, test and validation sets constructed by the provided scripts (see the list of patches with seasonal snow [here](http://bigearth.net/static/documents/patches_with_seasonal_snow.csv) and that of cloud and cloud shadow [here](http://bigearth.net/static/documents/patches_with_cloud_and_shadow.csv)).
Authors
-------
## Authors
[**Gencer Sümbül**](http://www.user.tu-berlin.de/gencersumbul/) [**Gencer Sümbül**](http://www.user.tu-berlin.de/gencersumbul/)
[**Tristan Kreuziger**](https://www.rsim.tu-berlin.de/menue/team/tristan_kreuziger/) [**Tristan Kreuziger**](https://www.rsim.tu-berlin.de/menue/team/tristan_kreuziger/)
# License ## License
The BigEarthNet Archive is licensed under the **Community Data License Agreement – Permissive, Version 1.0** ([Text](https://cdla.io/permissive-1-0/)). The BigEarthNet Archive is licensed under the **Community Data License Agreement – Permissive, Version 1.0** ([Text](https://cdla.io/permissive-1-0/)).
The code in this repository to facilitate the use of the archive is licensed under the **MIT License**: The code in this repository to facilitate the use of the archive is licensed under the **MIT License**:
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