Open Data Science | GeoHarmonizer project

Policy and decision making based on open data.
Models and predictions based on Open Source Software.
About project
Continental EU DTM at 30 m
Spatial analysis using FOSS4G
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Earth Observation Data
We use open EO data such as Sentinel imagery from the Copernicus Programme
in combination with cutting-edge Open Source Machine Learning to improve environmental layers in Europe.
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Machine Learning

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.

Open Earth Observation data

We base harmonization of geographic data (EU scale) on using the EO Sentinel 1/2 and Landsat 6/7/8 layers at 10, 30 and 60 m: cloud-free mosaics (2000–2020) produced via the DIAS infrastructure.

Combining multiscource data

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.

Automated harmonization

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.