We use a variety of free and open source software solutions for geospatial data (FOSS4G) in combination with state-of-the-art Machine Learning Algorithms as implemented in the R and Python programming environments. We run processing within EU-supported High Performance Computing (HPC) / and similar Cloud computing infrastructures.
We combine multiscource data — administrative layers, expert-based land cover maps, forest altases and maps, various type of legacy data — with huge volumes of time-series remote sensing images to produce statistical unbiased predictions of target variables and estimate realistic measures of uncertainty.
We develop tools for automated harmonization/conversion of variables including automated upscaling, downscaling, resolving of edge-problems and cross-border problems, imputing of missing values etc.
Our team (code and data sets developers, application developers) include experts in Open Source GIS, applied spatial analysis, Machine Learning, automated mapping and applied scientific fields such as vegetation, soil, land cover mapping and web-services.