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CORDIS

EVOLUTION OF THE COPERNICUS LAND SERVICE PORTFOLIO INTEGRATING NOVEL EO DATA AND LATEST MACHINE LEARNING ALGORITHMS TO CONTINUOUSLY MONITOR THE STATUS, DYNAMICS AND BIOMASS OF THE LAND SURFACE

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Deliverables

Data management plan - interim version (opens in new window)

Document describing how to implement the FAIR data principles, as well as data types, licenses and formats used within the project.

Best practices for running large scale EO processes in the cloud - final version (opens in new window)

Report with best practices on algorithm and code design for cloud processing.

Infrastructure requirements report - final version (opens in new window)

Report with an inventory of the infrastructure required for operational implementation/ of the next-generation CLMS services, and for the project’s methodological developments and prototypic implementations.

EO missions catalogue relevant for EvoLand - final version (opens in new window)

Inventory of available relevant EO data over EvoLand test and prototype sites. D2.1 is complementary to D1.2, but is viewed from the supplier side, while D1.2 covers the demand side.

Visual identity, project branding and website (opens in new window)

Report describing visual elements used in the project as well as the branding materials and website.

Publications

DeCUR: decoupling common & unique representations for multimodal self-supervision (opens in new window)

Author(s): Yi Wang, Conrad M Albrecht, Nassim Ait Ali Braham, Chenying Liu, Zhitong Xiong, Xiao Xiang Zhu
Published in: 2023
Publisher: Computer Vision and Pattern RecognitionComputer Vision and Pattern RecognitionComputer Vision and Pattern RecognitionComputer Vision and Pattern RecognitionComputer Vision and Pattern RecognitionComputer Vision and Pattern RecognitionComputer Vision and P
DOI: 10.48550/arXiv.2309.05300

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