Public Earth Observation datasets such as Sentinel satellite imagery and Copernicus Land Monitoring Service are an essential source for information about climate processes. Copernicus Data Space Ecosystem (CDSE) enables free public access and cloud-based analysis.
We will learn how to create visualisations in Copernicus Browser, a web browser based free tool that also supports custom code. Datasets of true color and radar imagery, thermal maps and air pollution rasters will be explored together with ready-made data products for land cover and various bio-geophysical parameters. Static, shareable images, 3D views, timelapse animations and web-ready OGC services will be shown as creating output products.
Next, we will dive into custom analysis and visualisation algorithms. AI assistants are highly proficient with CDSE APIs, so changing color scales, combining products of different algorithms, analysing several data sources together are all within reach even for people with no prior coding experience.
Finally, we will explore the Jupyter Lab as a platform for quantitative analysis and map integration. CDSE supports tools that enable the calculation of zonal statistics, time series or even advanced classification. Again, AI assistants can be used for natural language based code development and output product validation.
By the end of this course, you will be equipped for performing data analysis on one of the largest public cloud repositories of earth observation data globally, and for further improving your skills independently.