Analysis of Big Earth Data with Jupyter notebooks

This repository hosts the Jupyter notebooks developed for the lecture on 'Analysis of Big Earth Data with Jupyter notebooks' during the OpenGeoHub Summer School 2020.

What is this lecture about?

Growing volumes of Big Earth Data force us to change the way how we access and process large volumes of geospatial data. New (cloud-based) data systems are being developed, each offering different functionalities for users.

This lecture is split in two parts:

• (Cloud-based) data access systems
  This part will highlight five data access systems that allow you to access, download or process large volumes of Copernicus data related to climate and atmosphere. For each data system, an example is given how data can be retrieved. Data access systems that will be covered:

  • Copernicus Climate Data Store (CDS) / Copernicus Atmosphere Data Store (ADS)
  • WEkEO - Copernicus Data and Information Access System
  • Open Data Registry on Amazon Web Services
  • Google Earth Engine

• Case study: Analysis of Covid-19 with Sentinel-5P data
  This example showcases a case study analysing daily Sentinel-5P data from 2019 and 2020 with Jupyter notebooks and the Python library xarray in order to analyse possible Covid-19 impacts in 2020.

Lecture outline

This lecture has the following outline:

• 01 - Introduction to Project Jupyter (optional)

• 02 - Copernicus Climate Data Store / Copernicus Atmosphere Data Store

• 03 - WEkEO - Copernicus Data and Information Access Service (DIAS)

• 04 - Amazon Web Services Open Data Registry

• 05 - Google Earth Engine

• 11 - Covid-19 case study - Sentinel-5P anomaly map

• 12 - Covid-19 case study - Sentinel-5P time-series analysis