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Open Data - why is it important?

Open Data is NOT only data that you can access without paying a fee or similar.

Google Maps distributes, for example, proprietary data which neither has an open license, nor can be used without dealing with complex permissions and terms of use. OpenStreetMap, on the other hand, is a genuine Open Data project. Open data and content can be freely used, modified, and shared by anyone for any purpose. Not every data is “Open” even though the data producers can claim full openness. An open work must satisfy the following requirements in its distribution: 

M1 Matrix comparison of technology for automated data harmonization
Geo-harmonizer project Prepared by: mundialis, OpenGeoHub, CTU in Prague and MultiOne
Geo-harmonizer project implementation plan 2020–2022
Prepared by: OpenGeoHub, CTU in Prague, Terrasigna, mundialis and MultiOne
Geo-harmonizer project milestones

Implementation plan 2020–2022.

Geo-harmonizer project has 14 milestones, which are linked with the project outputs.

Geo-harmonizer project outputs

Implementation plan 2020–2022.

Geo-harmonizer project has 20 outputs, which are linked with the project milestones.

2.a: Fully functional metadata-catalogue

Geo-harmonizer project

Prepared by: mundialis, OpenGeoHub, CTU in Prague and MultiOne

Executive summary

A CSW-compliant metadata catalogue for the Geo-harmonizer project is available for testing at

Building Continental Europe Digital Terrain Model at 30 m resolution using Machine Learning admin Tue, 09/29/2020 - 10:57

Development team: OpenGeoHub, MultiOne,

The Geo-harmonizer project requires an up-to-date terrain model of Continental Europe that can be used for land cover, vegetation and soil mapping. Currently at least three public global Digital Elevation Models exist that cover Europe and are available for spatial modeling:

1.a: Generic software package (eumap) as a package in Python, R and similar

Geo-harmonizer project

Prepared by: MultiOne, OpenGeoHub, CTU in Prague and mundialis

Executive summary

eumap is a generic software solution which wraps together functions for automated mapping, harmonization and generation of predictions using EO raster layers. The package can be accessed via: It includes:

A benchmark dataset for mapping land-cover in Europe

Mapping the land-cover (LC) dynamics over the past two decades (2000 to 2020) for Continental Europe is one of the main goals of the Geo-Harmonizer project. To generate annual predictions of land cover, we will use a spatiotemporal machine learning model, able to generalize in space and time, to predict the LC in any part of the European territory over different years.

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