Data Cubes are an idealized concept of preprocessed, same resolution, aligned, and stacked raster datasets to support fast large-scale analyses based on map algebra. The topic of this thesis is to reproduce an analysis and reporting workflow under the framework of open science and FAIR (Findable, Accessible, Interoperable, Reproducible) principles. The thesis, while technical, also requires developing a strong theoretical overview and assessment of the FAIR principles, cloud native geospatial data and processing technologies, the data cube concept, and data and metadata versioning.
The topic is suitable for master students specialized in geoinformatics. The topic requires Python scripting skills (LTOM.02.041 Geospatial Analysis with Python and R), and the candidate is expected to have a wider understanding of the modern geospatial ecosystem (LTOM.02.043 Spatial Data Infrastructures / LTOM.02.067 Spatial Data on the Web). Co-supervisors Evelyn Uuemaa, Marta Jemeljanova
