During the workshop, we will present the Copernicus Data Space Ecosystem, a European project for sharing, storing, and computing EO data from the Copernicus program. Participants will learn about the basic functions of the Ecosystem, which includes several components such as Copernicus Browse, for easy visualization and analysis of imagery data from various Sentinel missions, and Sentinel Hub’s QGIS plugin, for convenient creation and display of EO imagery compositions in QGIS software. These tools provide users with access to worldwide historical and current imagery data without requiring specific technical knowledge. Additionally, participants will be introduced to cloud computing in the CDSE using Jupyter Labs. We will present two use cases utilizing OpenEO, a library that allows for easy access to and processing of Copernicus program data. Each use case will demonstrate the power of cloud computing and the easy access to Sentinel-2 data.

The latest addition to the data collections available on the Copernicus Data Space ecosystem is the Copernicus Contributing Missions data. These missions encompass existing or planned commercial missions from EU Member States or Copernicus Participating States, commercial operators of Very High Resolution (VHR) optical and radar missions, and other emerging European mission operators that provide some of their data for Copernicus. This collection is particularly interesting for the OpenStreetMap volunteer community due to the availability of high-resolution optical images that can serve as a basemap for vectorization. The WMS services to be presented combine data discovery, access, (pre)-processing, publishing (rendering), and dissemination capabilities available within a single RESTful (Representational state transfer) query. This gives a user great flexibility in terms of on-the-fly data extraction across a specific AOI (Area Of Interest), mosaicking, reprojection. The performance of the Copernicus Data Space Ecosystem and CREODIAS platform combined with efficient open software (Postgres 15 with PostGIS extension, MapServer with GDAL backend) allows achieving WMS service response times below 1 second on average. This, in turn, provides potential for massive parallelization of computations given the horizontal scaling of the Kubernetes cluster, and high availability of the data to be used without the need for downloading the original data in the most common spatial data editors such as QGIS and JOSM.