Methods
Evoland will develop algorithms and methods required for the candidate CLMS services using existing and novel Earth Observation (EO) data, in-situ data and infrastructure. A novel, modular, scalable and open-source IT infrastructure will be established. The datasets and infrastructure will be used to develop new algorithms for Weakly Supervised Learning (WSL), data fusion as well as continuous monitoring, biomass mapping and on-demand processing. The methods and algorithms developed will be demonstrated and assessed on test sites located both within and outside Europe.
Artificial intelligence
Innovative, state-of-the-art methodology to reduce the size and complexity of Copernicus Sentinel 1 & 2 EO data making it more accessible to core downstream...
Biomass mapping
New methods to map above-ground biomass in forests, and Gross Primary Production mapping in cropland and grasslands.
Novel EO data
Evaluating the full potential of space-based novel EO Data to meet Copernicus user needs and efficiently develop new products or improve existing...
Novel IT
Scalable, stable and cost-effective cloud infrastructure.
Novel in-situ data
We are compiling existing sources of in situ data and filling in data gaps, where necessary, by using novel approaches and tools for training data...
Continuous monitoring
From (multi-)annual snapshots to continuous monitoring of forestry, urban and Land Use, and Land Cover Change (LULCC) products.
Data fusion
Fusing data to improve spatial, spectral and temporal resolution
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