neteler / jupyter_sentinel2_grass_gis Goto Github PK

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 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
• 

Standard Jupyter Notebook for local usage or in Jupyterhub:

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

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)