Technically, EvoLand evaluates weakly supervised deep learning methodologies as demonstrators for integration with Copernicus Land Monitoring Service. Prototypes leverage novel Earth Observation and in-situ data for the application of the latest Machine Learning techniques to continuously monitor the status, dynamics and biomass of the land surface.
Since 2011, the Copernicus Land Monitoring Service (CLMS) provides core products for the monitoring of status and changes in vegetated and non-vegetated land cover/land use state and characteristics, biophysical variables, water, and cryosphere conditions.
Under the new Copernicus 2.0 programme, CLMS will continue to provide its core Copernicus information products and services for the 2021-2027 period, thus building time series that enable for long term change monitoring. Additionally, harmonisation and further integration of the products across components (local/pan-European/global) is envisaged in the programme as well as a more efficient workflow by further automation, modularity of the software and data products, and the increased use of Artificial Intelligence (AI) to provide real and near real time data processing, to respond to emerging European policy needs.
EVOLAND
EvoLand will develop and test new and innovative methods, algorithms and candidate Copernicus Land Monitoring Service prototypes by integrating novel EO/in-situ data and latest Machine Learning techniques to continuously monitor the status, dynamics and biomass of the land surface.
