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README.md

-# PySegCNN: Image segmentation with convolutional neural networks in Python
-This repository hosts a Python package called ``pysegcnn``. The package is
-primarily designed to build deep fully convolutional neural networks for
-semantic image segmentation of multispectral satellite imagery. ``pysegcnn``
-is based on the machine learning framework [PyTorch](https://pytorch.org/).
-
-## Requirements
-``pysegcnn`` requires **Python 3.7** or greater.
-
-## Installation
-You can download ``pysegcnn`` from [this repository's website](https://gitlab.inf.unibz.it/REMSEN/ccisnow/pysegcnn)
-or alternatively use ``git`` from terminal:
-
-```bash
-git clone https://gitlab.inf.unibz.it/REMSEN/ccisnow/pysegcnn.git
-```
-This creates a copy of the repository in your current directory on the file
-system.
-
-To install ``pysegcnn``, I recommend to use the ``conda`` package manager.
-You can download ``conda`` [here](https://docs.conda.io/en/latest/miniconda.html).
-Once successfully installed ``conda``, I recommend to add ``conda-forge`` as
-your default channel:
-
-```bash
-conda config --add channels conda-forge
-```
-To finally install ``pysegcnn``, I recommend to create a specific ``conda``
-[environment](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html),
-by using the provided ``environment.yml`` file. In a terminal, navigate to the
-**cloned git repositories root directory** (``/pysegcnn``) and type:
-
-```bash
-conda env create -f environment.yml
-```
-This may take a while. The first line in ``environment.yml`` defines the
-environment name, in this case ``pysegcnn``. Activate your environment using:
-
-```bash
-conda activate pysegcnn
-```
-After activating your environment, install the version of PyTorch and CUDA
-that your system supports by following this [guide](https://pytorch.org/get-started/locally/).
-Having [successfully installed](https://pytorch.org/get-started/locally/#linux-verification)
-PyTorch, type:
-
-```bash
-pip install -e .
-```
-Make sure you run the above command **from this repositories root directory
-within the activated ``pysegcnn`` conda environment**. If successful,
-you should be able to import ``pysegcnn`` from any Python interpreter using
-
-```python
-import pysegcnn
-```
-
-## Datasets
-Currently, the following publicly available satellite imagery datasets are
-supported out-of-the-box:
-
-- Spatial Procedures for Automated Removal of Cloud and Shadow ([SPARCS](https://www.usgs.gov/land-resources/nli/landsat/spatial-procedures-automated-removal-cloud-and-shadow-sparcs-validation)) by Hughes M.J. & Hayes D.J. ([2014](https://www.mdpi.com/2072-4292/6/6/4907))
-- [Cloud-38](https://github.com/SorourMo/38-Cloud-A-Cloud-Segmentation-Dataset)
-and [Cloud-95](https://github.com/SorourMo/95-Cloud-An-Extension-to-38-Cloud-Dataset)
-by Mohajerani S. & Saeedi P. ([2019](https://arxiv.org/abs/1901.10077), [2020](https://arxiv.org/abs/2001.08768))
-
-## Contributors & Contact
-- Daniel Frisinghelli
-
-For further information or ideas for future development please contact:
-daniel.frisinghelli@gmail.com.
-
-## License
-If not explicitly stated otherwise, this repository is licensed under the
-**GNU GENERAL PUBLIC LICENSE v3.0**
-(see [LICENSE](https://gitlab.inf.unibz.it/REMSEN/ccisnow/pysegcnn/-/blob/master/LICENSE)).
-
-## Acknowledgements
-I wrote a part of the code base for the ``pysegcnn`` package while I was working
-at the [Institute for Earth Observation](http://www.eurac.edu/en/research/mountains/remsen/Pages/default.aspx) of
-[Eurac Research](http://www.eurac.edu/en/Pages/default.aspx), Bolzano.

README.rst

+#########################################################################
+PySegCNN: Image segmentation with convolutional neural networks in Python
+#########################################################################
+
+``pysegcnn`` is a Python package to build deep fully convolutional neural
+networks for semantic image segmentation. The package is primarily designed to
+work with multispectral satellite imagery. ``pysegcnn`` is based on the machine
+learning framework `PyTorch <https://pytorch.org/>`_.
+
+.. include:: docs/source/installation.rst
+
+Datasets
+========
+Currently, the following publicly available satellite imagery datasets are
+supported out-of-the-box:
+
+- Spatial Procedures for Automated Removal of Cloud and Shadow `SPARCS`_
+by `Hughes M.J. & Hayes D.J. (2014)`_
+- `Cloud-38`_ and `Cloud-95`_ by Mohajerani S. & Saeedi P. (`2019`_, `2020`_)
+
+Contact
+=======
+For further information or ideas for future development please contact:
+daniel.frisinghelli@gmail.com.
+
+License
+=======
+If not explicitly stated otherwise, this repository is licensed under the
+**GNU GENERAL PUBLIC LICENSE v3.0**
+(see `LICENSE <https://gitlab.inf.unibz.it/REMSEN/ccisnow/pysegcnn/-/blob/master/LICENSE>`_).
+
+Acknowledgements
+================
+I wrote a part of the code base for the ``pysegcnn`` package while I was working
+at the `Institute for Earth Observation <http://www.eurac.edu/en/research/mountains/remsen/Pages/default.aspx>`_ of
+`Eurac Research <http://www.eurac.edu/en/Pages/default.aspx>`_, Bolzano.
+
+
+..
+    Links:
+
+.. _SPARCS:
+    https://www.usgs.gov/land-resources/nli/landsat/spatial-procedures-automated-removal-cloud-and-shadow-sparcs-validation)
+
+.. _Hughes M.J. & Hayes D.J. 2014:
+    https://www.mdpi.com/2072-4292/6/6/4907
+
+.. _Cloud-38:
+    https://github.com/SorourMo/38-Cloud-A-Cloud-Segmentation-Dataset
+
+.. _Cloud-95:
+    https://github.com/SorourMo/95-Cloud-An-Extension-to-38-Cloud-Dataset
+
+.. _2019:
+    https://arxiv.org/abs/1901.10077
+
+.. _2020:
+    https://arxiv.org/abs/2001.08768
\ No newline at end of file