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A collection of notebooks demonstrating EO operations. Designed to run on Jupyterhub

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eodag-notebooks

This repository stores an example gallery repo for loading and processing Sentinel Data from Copernicus Dataspace Ecosystem useing EODAG. Futhermore it is intended to be used on the Jupyterlab of TU Wien. Therefore the Makefile and setup.py are provided to make the setup easier on the Jupyterhub and should not be used on your local machine. If you want to use the Notebooks on your local machine, create a new conda environment from the environment.yml file and manually enter your credentials for CDSE or any other provider in the ~/.config/eodag/eodag.yml file.

The repo contains the following elements in the notebooks directory:

  • 01_eodag_search.ipynb A notebook just for the Search of EO Data
  • 02_eodag_des_post.ipynb A notebook for downloading the Data from serialized search results
  • 03_eodag_img_pro.ipynb A notebook for image processing
  • 04_eodag_search_post.ipynb A notebook for searching and downloading without serialization (the workflow as intended by EODAG)
  • 05_eodag_merging.ipynb A notebook for combining EO data temporarily and geospatialy
  • 06_eodag_classify.ipynb A notebook for different classification processes
  • 07_eodag_roi.ipynb A Notebook which shows how to create Geojsons of Regions of Interest.

Before getting started

In order to acquire and process Sentinel Data you will need to have an account at Copernicus Dataspace Ecosystem (CDSE). Use the provided Link or visit: https://dataspace.copernicus.eu/

Make sure that you are registered as a Natural Person. If you are registered as research organisation the download of the data is not possible.

Getting started on Jupyterlab

Copy the provided Makefile into your HOME directory. Open a Terminal (where you placed the Makefile) and type the following command.

make all

This command will:

  • pull the notebooks from the Git repository
  • execute the setup.py file from the repo (adds some folders to store data and creates a config file where your Credentials for CDSE will be stored)
  • create a conda environment from environment.yml file
  • and creates a kernel

So have your credentials for CDSE ready when running this command, or run the setup.py file later on to set your credentials. The makefile has a make setup command, which will do that for you.

After the setup has finished you will find the following files in your directory.

eodag-notebooks
├── Makefile
├── README.md
├── environment.yml
├── setup.py
├── notebooks
│   ├── 01_eodag_search.ipynb       # Searching for Data
│   ├── 02_eodag_des_post.ipynb     # Download Data from serialized Search
│   ├── 03_eodag_img_pro.ipynb      # Image Processing (Contrast, Stacking,...)
│   ├── 04_eodag_search_post.ipynb  # Search and Postprocessing
│   ├── 05_eodag_merging.ipynb      # Merging of overlapping Geographical extents
│   ├── 06_eodag_classify.ipynb     # Classification
│   ├── 07_eodag_roi.ipynb          # Polygons of regions of Interest
│   ├── paths.yml
│   └── paths_temp.yml
├── postprocess     # Directory where you can store your Results
├── serialize       # Directory where you can store serialized searches
└── shapefiles      # Directory where you can store shapefiles (geojson)

Here you find the Notebooks 01 - 07 each of them explains something different. It is advised, that you create a new notebook and use all the code-snippets which you would like to use yourself from the provided notebooks.

Permissions

As a student you have read and write permissions in your HOME directory. Furthermore you have reading rights in the shared directory. Here we have a directory shared/datasets/rs/datapool/download where we can store the data you would like to use for your work. As you do not have rights to download data there you have two options.

  1. Provide a search_results.geojson file (see Notebook 1) and we will download the data for you onto the Jupyterlab.
  2. Download the data to your home directory by changeing the download path in the paths.yml file.

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A collection of notebooks demonstrating EO operations. Designed to run on Jupyterhub

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