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

Informations projet

RapidAI4EO

N° de convention de subvention: 101004356

DOI 10.3030/101004356

Projet clôturé

Date de signature de la CE 16 Octobre 2020

Date de début 1 Janvier 2021

Date de fin 31 Mars 2023

Financé au titre de

• INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies – Space

Coût total € 1 498 987,50

Contribution de l’UE € 1 498 987,50

Coordonné par

VLAAMSE INSTELLING VOOR TECHNOLOGISCH ONDERZOEK N.V.
Belgium

Livrables

Demonstrators, pilots, prototypes (3)

Change Detection Demo

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

Open Research Data Pilot (2)

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

Documents, reports (3)

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

Websites, patent fillings, videos etc. (1)

Project and Product Website

Publications

Conference proceedings (3)

RapidAI4EO: A Corpus for Higher Spatial and Temporal Reasoning

Auteurs: Giovanni Marchisio, Patrick Helber, Benjamin Bischke, Timothy Davis, Caglar Senaras, Daniele Zanaga, Ruben Van De Kerchove, Annett Wania
Publié dans: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, Page(s) 1161-1164, ISBN 978-1-6654-0369-6
Éditeur: IEEE
DOI: 10.1109/igarss47720.2021.9553080

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

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

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

Auteurs: 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
Publié dans: IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 2022, ISBN 978-1-6654-2793-7
Éditeur: IEEE
DOI: 10.1109/igarss46834.2022.9883198