Commit a524c44a by Tristan Kreuziger

Updated links

parent d387b699
# BigEarthNet-19 Deep Learning Models
This repository contains code to use the BigEarthNet 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) 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.
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).
......@@ -21,7 +21,7 @@ The TensorFlow code for these models can be found [here](https://gitlab.tu-berli
The pre-trained models associated to other deep learning libraries will be released soon.
# Generation of Training/Test/Validation Splits
After downloading the raw images from https://www.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_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:
* `-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.
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment