changes in readme

README.md

 # Exploring Wilderness Using Explainable Machine Learning in Satellite Imagery
 
-This code belongs to the research article **"Exploring Wilderness Using Explainable Machine Learning in Satellite Imagery" (2022) by Timo T. Stomberg, Taylor Stone, Johannes Leonhardt, Immanuel Weber, and Ribana Roscher (https://doi.org/10.48550/arXiv.2203.00379)**.
+This code belongs to the research article **"Exploring Wilderness Using Explainable Machine Learning in Satellite Imagery" (2022) by Timo T. Stomberg, Taylor Stone, Johannes Leonhardt, Immanuel Weber, and Ribana Roscher; https://doi.org/10.48550/arXiv.2203.00379**.
 
-The **AnthroProtect** dataset with which the code has been tested can be found here: **http://rs.ipb.uni-bonn.de/data/anthroprotect** .
+The **AnthroProtect dataset** with which the code has been tested can be found here: **http://rs.ipb.uni-bonn.de/data/anthroprotect** .
 
 The **trained model** used for our publication can be downloaded here: **http://rs.ipb.uni-bonn.de/downloads/asos/** .
 
-**Please cite this article, if you use this code, the model or the dataset.**
+**Please cite our article, if you use this code, the model or the dataset.**
 
 <br>
 <img src="readme/graphical_abstract.png">
@@ -17,7 +17,7 @@ This readme file is structured as follows:
  - Code Structure
  - Summary AnthroProtect Dataset
  - Summary Activation Space Occlusion Sensitivity (ASOS)
- - Setup and Requirements
+ - **Setup and Requirements**
  - **Getting Started: Easily Predict a Sensitivity Map using a Trained Model**
  - Train Your Own Model
  - Export Your Own Data
@@ -83,26 +83,26 @@ Please download or clone this repository on your machine. Use the environment.ym
 
 If you work on an Ubuntu system and use Anaconda, you can easily use the setup.sh file to set up everything including the Python paths. Just run the following command in your terminal:
 
- - `source setup.sh`
+`source setup.sh`
 
 Enter "1) install venv from yml". Next time you can enter "2) activate venv".
 
 Before you run setup.sh the first time, make sure that you have installed Anaconda and the following packages or run the following lines:
 
- - sudo apt install python3.8-venv python3-pip
- - sudo apt-get install libgl1-mesa-glx libegl1-mesa libxrandr2 libxrandr2 libxss1 libxcursor1 libxcomposite1 libasound2 libxi6 libxtst6
- - sudo apt update
- - sudo apt upgrade
+`sudo apt install python3.8-venv python3-pip`<br>
+`sudo apt-get install libgl1-mesa-glx libegl1-mesa libxrandr2 libxrandr2 libxss1 libxcursor1 libxcomposite1 libasound2 libxi6 libxtst6`<br>
+`sudo apt update`<br>
+`sudo apt upgrade`
 
 You can also install this repository to your own project using **pip**. This way you can access the whole tlib library; but you cannot access the Jupyter Notebooks to reproduce our work.
 
- - **pip install git+https://gitlab.jsc.fz-juelich.de/kiste/wilderness@main**
+`pip install git+https://gitlab.jsc.fz-juelich.de/kiste/asos@main`
 
 
 ## Getting Started: Easily Predict a Sensitivity Map using a Trained Model
 
-- Download the AnthroProtect dataset. You find a link at the beginning of this readme. Unzip the dataset.
-- Download the trained model (link also at the beginning of this readme). Unzip the zip file and locate the "logs" folder in a working directory of your choice. Within this working directory, other files (figures etc.) might be saved later on.
+- **Download** the AnthroProtect dataset. You find a link at the beginning of this readme. Unzip the dataset.
+- **Download** the trained model (link also at the beginning of this readme). Unzip the zip file and locate the "logs" folder in a **working directory** of your choice. Within this working directory, other files (figures etc.) might be saved later on.
 - Please open the file "projects/**main_config.py**" and set the configurations as described in this file.
 <br><br>
 - Setup the repository as described in "Setup and Requirements", e.g. running `source setup.sh` in your terminal.
@@ -111,11 +111,11 @@ You can also install this repository to your own project using **pip**. This way
 
 If you have a Google Earth Engine account, you can predict a sensitivity map of any region of your choice:
 
- - Open the notebook "projects/asos/35_analyze_any_region.ipynb". Run the cells and follow the descriptions.
+ - Open the notebook "projects/asos/**35_analyze_any_region.ipynb**". Run the cells and follow the descriptions.
 
 Otherwise (or additionally):
 
- - Open the notebook "projects/asos/34_analyze_samples.ipynb". You have several options to load the data and to plot them.
+ - Open the notebook "projects/asos/**34_analyze_samples.ipynb**". You have several options to load the data and to plot them.
 
 
 ## Train Your Own Model
@@ -128,6 +128,6 @@ Otherwise (or additionally):
 
 If you want to export your own data, have a look in the folder "projects/anthroprotect". You can define paramaters such as the countries in the config.py file. Run the notebooks in the given order.
 
-The workflow of the Google Earth Engine data export is sketched in the following diagram:
+The **workflow** of the Google Earth Engine data export is sketched in the following diagram:
 
 <img src="readme/gee_workflow.svg">