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.

As part of the research carried out into selected aspects of database quality OSM database, an analysis of a collection of address points collected in the database of OpenStreetMap (OSM) database for selected administrative units of Poland. This presentation presents the obtained results of the analysis. Validation was performed using appropriately developed tool and based on comparison of address data with the reference database State Register of Borders (PRG). The analysed descriptive and spatial features of the address data in the OSM, such as the name of the of locality, street, building number and coordinates, were compared in a specific way with analogous metadata from the PRG database. The analysis using an appropriately designed tool aimed to improve the quality of address data in OSM through systematic validation and comparison with reference data, as well as to developing good practices in the context of working with address data. A number of interesting results and conclusions in this respect. Furthermore, on the basis of the conducted tests and analyses, it was confirmed, among other things, that the state of addresses in OpenStreetMap depends on the the involvement of the community, which takes care of the integrity of the points. Relevant examples, including the possession of very good quality data superior to the timeliness of the state equivalent. Needs and directions for further research were pointed out.