RapidAI4EO - Advancing the State-of-the-Art for Rapid and Continuous Land Monitoring | RapidAI4EO | Project | Results | H2020 | CORDIS

Rezultaty

Demo for Change Detection with interactive heatmap visualizations showing the automatically identified changes in CLC classes

Demo of RapidAI4EO

Demonstration of high cadence change detection and improved LULC mapping on the ONDA DIAS platform

Tentative POC with VHR imagery other than PlanetScope

A proof of concept of the RAPIDAI4EO framework with VHR data other than PlanetScope

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

Project and Product Website

Publikacje

RapidAI4EO: A Corpus for Higher Spatial and Temporal Reasoning

Autorzy: Giovanni Marchisio, Patrick Helber, Benjamin Bischke, Timothy Davis, Caglar Senaras, Daniele Zanaga, Ruben Van De Kerchove, Annett Wania
Opublikowane w: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, Strona(/y) 1161-1164, ISBN 978-1-6654-0369-6
Wydawca: IEEE
DOI: 10.1109/igarss47720.2021.9553080

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

Autorzy: Timothy Davis, Benjamin Bischke, Patrick Helber, Giovanni Marchisio, Caglar Senaras, Daniele Zanaga, Ruben Van de Kerchove, Annett Wania
Opublikowane w: Proceedings of the 2021 conference on Big Data from Space, 2021, Strona(/y) 65-68, ISBN 978-92-76-37661-3
Wydawca: Publications Office of the EU
DOI: 10.2760/125905

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

Autorzy: 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
Opublikowane w: IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 2022, ISBN 978-1-6654-2793-7
Wydawca: IEEE
DOI: 10.1109/igarss46834.2022.9883198

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

Rezultaty

Publikacje

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