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RapidAI4EO - Advancing the State-of-the-Art for Rapid and Continuous Land Monitoring

Risultati finali

Packaged Augmented SpatioTemporal Training Corpus for open sourcing

Package all datasets generated from Tasks 16 in preparation for open sourcing

Packaged SpatioTemporal Training Corpus for open sourcing

Package all datasets generated to date in preparation for open sourcing

Monthly accuracy of change maps at 3 and 10 m res

Validation of monthly heat maps of change over Europe

Monthly accuracy of CLC maps at 3 and 10 m res

Validation of monthly LULC maps for the 3 AOIs

Implementation roadmap

An assessment of the operationalisation of the demonstrated service in terms of its readiness for an operational deployment in the future in particular its costbenefit calendar and operational feasibility

Pubblicazioni

RapidAI4EO: A Corpus for Higher Spatial and Temporal Reasoning

Autori: Giovanni Marchisio, Patrick Helber, Benjamin Bischke, Timothy Davis, Caglar Senaras, Daniele Zanaga, Ruben Van De Kerchove, Annett Wania
Pubblicato in: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, Pagina/e 1161-1164, ISBN 978-1-6654-0369-6
Editore: IEEE
DOI: 10.1109/igarss47720.2021.9553080

Rapidai4EO: A Multi-Format Dataset For Automated Land Cover Classification And Change Detection

Autori: Timothy Davis, Benjamin Bischke, Patrick Helber, Giovanni Marchisio, Caglar Senaras, Daniele Zanaga, Ruben Van de Kerchove, Annett Wania
Pubblicato in: Proceedings of the 2021 conference on Big Data from Space, 2021, Pagina/e 65-68, ISBN 978-92-76-37661-3
Editore: Publications Office of the EU
DOI: 10.2760/125905

Rapidai4Eo: Mono-and Multi-Temporal Deep Learning Models for Updating the Corine land Cover Product

Autori: P. Bhugra, B. Bischke, C. Werner, R. Syrnicki, C. Packbier, P. Helber, C. Senaras, A. S. Rana, T. Davis, W. De Keersmaecker, D. Zanaga, A. Wania, R. Van de Kerchove, G. Marchisio
Pubblicato in: IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 2022, ISBN 978-1-6654-2793-7
Editore: IEEE
DOI: 10.1109/igarss46834.2022.9883198

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