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https://github.com/neteler/jupyter_sentinel2_grass_gis

Jupyter notebook: GRASS GIS 8 and processing of multitemporal EO data
https://github.com/neteler/jupyter_sentinel2_grass_gis

google-colab-notebook grass-gis jupyter-notebook machine-learning python randomforest-classification sentinel-2 time-series-analysis

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Jupyter notebook: GRASS GIS 8 and processing of multitemporal EO data

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## Jupyter notebook: GRASS GIS 8 and processing of multitemporal EO data



by Markus Neteler, https://www.mundialis.de/



presented at:



*Open Data Science Europe workshop 2022, 2022-06-14, 11:00-12:30, Workshop room 2 - C223*



https://opendatascience.eu/workshop-2022/



**Abstract**



GRASS GIS supports time-series processing for vector, raster, and volume data. This workshop offers a micro-introduction to Sentinel satellite data archives, and the various ways to access them. It also explores the i.sentinel toolset which allows querying Sentinel data coverage for a region of interest, downloading from multiple data sources, performing atmospheric and topographic corrections, and cloud/shadow masking. This workshop also gives a preparation of data for multitemporal analyses through automatic creation of a space-time raster dataset (strds), It explores the computation of NDVI time series. Eventually we run a simple RandomForest landuse classification on Sentinel-2 data.



This course is based on [workshop material](https://geo.fsv.cvut.cz/geoharmonizer/odse_workshop_2022/grass/html/units/06.html) by Martin Landa, CVUT.



We will run GRASS GIS 8.2 through a Jupyter Notebook.



**Content**



- Why Jupyter Notebooks and how to use them?

- GRASS GIS & Python

- Setup of the Google Colab instance with GRASS GIS 8 *(only Google Colab version of Jupyter Notebook)*

- Paths and variables, connecting to GRASS GIS backend

- Data upload to notebook session

- Initialization of GRASS GIS in the Jupyter notebook session

- Creating an area of interest map

- Importing geodata into GRASS GIS

- Sentinel-2 processing overview

- Computing NDVI

- Time series data processing

- Creating an image stack (imagery group)

- Object recognition with image segmentation

- Supervised Classification: RandomForest

- What's next?



**Jupyter Notebooks**



Google Colab (https://colab.research.google.com/) notebook version:

- https://github.com/neteler/jupyter_sentinel2_grass_gis/blob/main/sentinel2_grass_gis_colab.ipynb

- [![Google Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neteler/jupyter_sentinel2_grass_gis/blob/main/sentinel2_grass_gis_colab.ipynb)



Standard Jupyter Notebook for local usage or in Jupyterhub:

- https://github.com/neteler/jupyter_sentinel2_grass_gis/blob/main/sentinel2_grass_gis.ipynb



## Overview: Executing Jupyter notebooks



There are several options available for running Jupyter notebooks, each offering different functions. Here are some ways to run Jupyter notebooks:



A) Your **local computer**:

- Standard Jupyter Notebook Server: Install the `python3-jupyter-core` server locally to run Jupyter notebooks



B) **Cloud providers** to use your own Jupyter notebooks without having to install anything on your local machine:

- Binder: run Notebooks stored in GitHub, GitLab, Google Drive, etc. (https://mybinder.org/)

- Whole Tale: mainly for research purposes (https://wholetale.org/)

- Google Colab: run Notebooks stored in Google Drive and GitHub; offers good hardware options (CPU, GPU, TPU) (https://colab.research.google.com/)

- and other providers



C) Own **JupyterLab server**:

- JupyterLab: Deployment of a notebook server in your own server infrastructure, for multiple users (https://jupyter.org/hub)