SC 4.17 | Taming Big Earth Data with cloud-based processing on WEkEO
EDI
Taming Big Earth Data with cloud-based processing on WEkEO
Co-organized by ESSI2/GM12
Convener: Anna-Lena Erdmann | Co-convener: Ben Loveday
Thu, 01 May, 16:15–18:00 (CEST)
 
Room -2.62
Thu, 16:15
WEkEO offers a single access point to all of the environmental data provided by the Copernicus programme, as well as additional data from its four partner organisations. While data access is the first step for research based on EO data, the challenges of handling data soon become overwhelming with the increasing volume of Earth Observation data available. To cope with this challenge and to tame the Big Earth Data, WEkEO offers a cloud-based processing service for Earth Observation data coming from the Copernicus programme and beyond.
This course will explain new trends and developments in accessing, analysing and visualizing earth observation data by introducing concepts around serverless processing, parallel processing of big data and data cube generation in the cloud.
The session will begin with a theoretical introduction to cloud-based big data processing and data cube generation, followed by a demonstration how the participants can utilize these concepts within the WEkEO environment using its tools. Participants will have the opportunity to apply the concepts and tools in multi-disciplinary environmental use cases bringing together different kinds of satellite data and earth observation products as data cubes in the cloud.
The course will start with a beginner-level introduction and demonstration before introducing more advanced functionalities of the WEkEO services. Prior knowledge of satellite data analysis/Python programming would be an advantage but is not a prerequisite. Comprehensive training material will be provided during the course to ensure that participants with varying degree of knowledge of data processing can follow and participate.

You will follow the tutorial on your own laptop. Please create a WEkEO account before the short course. 

Session assets

Session materials