Innovation and Networks Executive Agency

2018-EU-IA-0095

Geo-harmonizer: EU-wide automated mapping system for harmonization of Open Data based on FOSS4G and Machine Learning
Programme: 
CEF Telecom
Call year:
Location of the Action:
Implementation schedule: 
September 2019 to June 2022
Maximum EU contribution: 
€1,423,864
Total eligible costs: 
€1,898,485
Percentage of EU support: 
75%
Coordinator: 

Czech Technical University in Prague (Czech Republic)
https://www.cvut.cz/en

Status:
DSI:
Additional information: 

Digital Single Market (DSM) strategy
http://ec.europa.eu/priorities/digital-single-market

DSM - Connecting Europe Facility
http://ec.europa.eu/digital-single-market/connecting-europe-facility

CEF Digital portal
https://ec.europa.eu/cefdigital

Innovation and Networks Executive Agency (INEA)
http://inea.ec.europa.eu

EU Open Data Portal
http://open-data.europa.eu/en/data/

Last modified: 
July 2020

2018-EU-IA-0095

The overall objective of the Action is to develop an original, web-based, scalable and modular system ("Geo-harmonizer") for hosting and accessing various thematic geospatial data layers (vector and raster GIS layers) to support cross-border services over the entire continental Europe.

The beneficiaries will create a data portal and a software suite extending a wide variety of free and open source software solutions for geospatial data (FOSS4G) in combination with state-of-the-art Machine Learning Algorithms, and will be made available within EU-supported High Performance Computing (HPC)/Cloud computing infrastructures.

The functionality of the system will be demonstrated vis-à-vis a list of new, added-value, pan-EU data sets including seamless continental Europe cover time-series (2000-2020), environmental quality indicators, potential natural vegetation maps and OpenStreetMap+ (improved continental Europe version of the OpenStreetMap). The data generated by the Action will be integrated into the European Data Portal.

The outcome of the Action is to use the Geo-harmonizer to connect state-of-the-art remote sensing data sources (Sentinel-2, Landsat, and similar), machine learning framework, cloud, and High Performance Computing and which will result in a significant reduction of processing and delivery time.