Marcin Niemyjski
As a trained surveyor, I have refined my skills in data analysis and interpretation, which I have applied in my transition towards becoming a Junior Data Scientist. Over the past two years, I have delved into GIS, remote sensing, LiDAR, and data processing, which have become my primary areas of interest. My vision for the spatial data industry resembles a puzzle, where open datasets, Python, SQL, and Machine Learning techniques are the pieces that need to interlock. In my view, the future of GIS is anchored in Big Data solutions and cloud computing, and I am fortunate to be cultivating my skills in this direction while working at CloudFerro.
Sessions
The Copernicus Data Space Ecosystem provides open access to the petabyte-scale EO data repository and to a wide range of tools and services, limited to some predefined quoatas. For users who would like to develop commercial services or for those who would like to have larger quotas/unlimited access to services the offer of CREODIAS platform is the solution. In this study an example of such a (pre)commercial service will be presented which publishes Copernicus Sentinel-1 and Sentinel-2 products (and selected assets) in the form of a WMS (Web Map Service) and WCS (Web Coverage Service). The architecture of the services based on the Kubernetes cluster allows horizontal scaling of a service along with a number of users requests. The WMS/WCS 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), mosaicing, reprojection, simple band processing (cloud masking, normalized difference vegetation), rendering. The performance of the Copernicus Data Space Ecosystem and CREODIAS platform combined with the efficient software (Postgres 16 with PostGIS extension, MapServer with GDAL backend) allows to achieve WMS/WCS service response time below 1 second on average. This in turn, gives a potential for massive parallelization of the computations given the horizontal scaling of the Kubernetes cluster. The work demonstrates the capabilities of European data processed using open software deployed on European cloud-based Ecosystem in form of CDSE.