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Come and join the next ODSE workshop in Prague, hosted by the Technical University of Prague (CTU).
The abstract submission is now open!
Deadline for submissions has been extended to May 1st !
A 5-day hybrid event with 16 training sessions for processing data cubes and using machine learning in Open Source, followed by 3 days of conference talks by leading European experts and researchers discussing challenges and opportunities for “Spatiotemporal modelling of European Landscapes and Climate 2000–2020: using EO and Machine Learning“.
The Open Data Science Europe data portal / viewer aims at serving decision-ready layers such land cover, air quality and pollution, potential natural vegetation and similar.
We use topographic data (DEMs), Earth observation (EO) data, hydrological and meteorological data that we map using automated mapping systems largely based on spatiotemporal Machine Learning algorithms available in the Open Source software. We then serve the data using Cloud-Optimized GeoTIFFs to allow for seamless and unrestricted access. We document all processing steps through gitlab.com up to the level of full-reproducibility!
The Copernicus land data products and similar data products funded by European Commission play a central role in the reference data framework that determines the quality and traceability of all Copernicus products and services. But many of these layers suffer from limited reproducibility and completeness. By building and implementing Geo-harmonizer we will contribute to the Copernicus product in producing new added-value products. For example, we use automated mapping methods to produce annual land cover maps at 30-m resolution for the complete period 2000–2020, the “OpenStreetMap+” (we merge OpenStreetMap data with other EO products) and a number of other value-added layers. We aim at producing analysis and/or decision ready layers that can be used directly by end-users.