Project description
Improved models for interpreting Earth observation data
The EU-funded GEM project will address the challenge of continuous monitoring of large Earth areas on regional and global scales in a sustainable and cost-effective way. The goal is to establish a new disruptive Earth observation data model that should significantly enhance the exploitation of Copernicus data. The project will further develop a proprietary concept of adjustable data cubes – a combination of static and dynamic data cubes, powered by Sentinel Hub – and integrate them with Earth observation data from the open-source machine learning framework eo-learn. Modern machine learning techniques will be combined to construct global, scale-independent interpretation models, with a special focus on causality and change detection.
Objective
Global Earth Monitor project (GEM) is addressing the challenge of continuous monitoring of large areas in a sustainable cost effective way. The goal of the project is to establish a new disruptive Earth Observation (EO) DATA-EXPLOITATION MODEL which will dramatically enhance the exploitation of Copernicus data. For the first time a continuous monitoring of the planet on the global/regional scale will be enabled for a sustainable price.
Disruptive innovations are planned in the technology and in the methodology domain, where a proprietary concept of Adjustable Data Cubes (a combination of static and dynamic data cubes) will be developed and integrated with EO-oriented open source Machine Learning (ML) framework EO-LEARN. During the project EO-LEARN will be upgraded to consume ML technologies from widely accepted ML frameworks and to adapt/evolve them to specifics of EO-data interpretation. Modern ML technologies and approaches (GAN, RNN, LSTM, Attention & Bayesian Deep Learning, Curriculum Learning, Incremental learning, Meta-learning, Hybrid modelling) will be combined to construct GLOBAL, SCALE-INDEPENDENT interpretation models with the special focus on CAUSALITY and CHANGE DETECTION.
Technological and Methodological innovations will be combined into a unique CONTINUOUS MONITORING PROCESS. The process, based on seamless combination of data interpreted with sub-resolution, native resolution and super-resolution methods, will deliver optimal combination of Processing/Storage costs – enabling continuous monitoring of large areas for just a FRACTION OF CURRENT COSTS.
The concept of continuous monitoring will be validated through the development of five specific use-cases and through their employment in a 10-month demonstration - operational continuous monitoring of 10 MIO square km area.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- natural sciencesphysical sciencesopticsmicroscopysuper resolution microscopy
- natural sciencesphysical sciencesastronomyplanetary sciencesplanets
- social scienceseconomics and businessbusiness and managementemployment
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Programme(s)
Funding Scheme
RIA - Research and Innovation actionCoordinator
1000 Ljubljana
Slovenia