Architecture of CREODIAS WMS Basemap for Very High Resolution Satellite Imagery from Copernicus Contributing Missions
07-20, 12:30–12:40 (Poland), Room CA4

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.

The availability of high-resolution data will be discussed, explaining their utility for the OSM community, and a proposed quick access method will be presented so that anyone can try them out in their favorite editor using the WMS standard.

🧠As a trained surveyor, I have honed my skills in data analysis and interpretation, which I have leveraged in my transition towards becoming a Junior Data Scientist. Over the last two years, I have been exploring the realm of GIS, remote sensing, LiDAR, and data processing, which have become my primary areas of interest. My vision of the spatial data industry is that of a puzzle with open datasets, Python, SQL, and Machine Learning techniques being the pieces that need to fit together. In my opinion, the future of GIS lies in Big Data solutions and cloud computing, and I am fortunate to be developing my skills in this direction while working at CloudFerro.

🌎I am familiar with:
👉🏻 Python programming - Numpy, PyQt, ArcPy libraries, GeoPandas, SciPy
👉🏻 PostgreSQL, Postgis
👉🏻 QGIS and ArcGIS
👉🏻Remote Sensing, EO
👉🏻 Extensive processing and interpretation of spatial data

😸Privately I am interested in drawing, cats, dogs and alternative rock.

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Junior Data Scientist at CloudFerro and Geoinformatic's student at Warsaw University of Technology. Mainly focused on developing solutions for sharing big spatial data via open-source technologies.

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