Satellites for Wilderness Inspection and Forest Threat Tracking

Informazioni relative al progetto

SWIFTT

ID dell’accordo di sovvenzione: 101082732

DOI

10.3030/101082732

Data della firma CE 18 Ottobre 2022

Data di avvio 1 Novembre 2022

Data di completamento 30 Aprile 2026

Finanziato da

• Digital, Industry and Space

Costo totale

€ 3 680 231,26

Contributo UE

€ 2 853 249,00

Coordinato da

AXA CLIMATE
France

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

Other (1)

Connected web and mobile platform

Documents, reports (6)

Report on First Feedback
Report on Second Feedback
Co-learning, internal communication and project management handbook, including detailed risk analysis and risk management plan

Colearning internal communication and project management handbook including detailed risk analysis and risk management plan

Intermediary report on dissemination and communication activities
Intermediary report with back-testing results
Intermediary report with model results checked on the ground

"Intermediate report with model results checked on the ground. New deadline requested: M38 (PLEASE NOTE WE CANNOT INPUT THIS MONTH IN THE ""DUE DATE"" FIELD OF THE ""DELIVERABLE DETAILS"", BUT THIS IS THE DEADLINE WE ENVISION WITH AN EXTENSION)"

Demonstrators, pilots, prototypes (1)

European-wide high fidelity forest cover maps

Europeanwide high fidelity forest cover maps

Pubblicazioni

Conference proceedings (8)

Machine learning models and technology for classification of forest on satellite data

Autori: Yevhenii Salii, Volodymyr Kuzin, Anton Hohol, Nataliia Kussul, Hanna Yailymova
Pubblicato in: ISBN 978-1-6654-6397-3
Editore: IEEE
DOI: 10.1109/EUROCON56442.2023.10199006

Features' Selection for Forest State Classification Using Machine Learning on Satellite Data

Autori: Yevhenii Salii; Volodymyr Kuzin; Alla Lavreniuk; Nataliia Kussul; Andrii Shelestov
Pubblicato in: ISSN 2153-7003
Editore: IEEE
DOI: 10.1109/IGARSS53475.2024.10642681

Monitoring of Fires Caused by War in Ukraine Based on Satellite Data

Autori: Bohdan Yailymov; Hanna Yailymova; Andrii Shelestov; Leonid Shumilo
Pubblicato in: ISBN 979-8-3503-9611-9
Editore: IEEE
DOI: 10.1109/DESSERT61349.2023.10416520

A multimodal dataset for forest damage detection and machine learning

Autori: Hanna Yailymova; Bohdan Yailymov; Yevhenii Salii; Volodymyr Kuzin; Nataliia Kussul; Andrii Shelestov
Pubblicato in: ISSN 2153-7003
Editore: IEEE
DOI: 10.1109/IGARSS53475.2024.10641873

Leveraging Sentinel-2 time series for bark beetle-induced forest dieback inventory

Autori: Giuseppina Andresini, Annalisa Appice, Donato Malerba
Pubblicato in: ISBN 9798400702433
Editore: Association for Computing Machinery
DOI: 10.1145/3605098.3635908

Potential of spectral-spatial analysis to map forest tree dieback due to bark beetle hotspots in Sentinel-2 images

Autori: Giuseppina Andresini; Annalisa Appice; Dino Ienco; Donato Malerba; Vito Recchia
Pubblicato in: ISSN 2153-7003
Editore: IEEE
DOI: 10.1109/IGARSS53475.2024.10641479

Semi-supervised forest type mapping in Europe on satellite data

Autori: Nataliia Kussul, Andrii Shelestov, Bohdan Yailymov, Hanna Yailymova
Pubblicato in: ISSN 2770-4254
Editore: IEEE
DOI: 10.1109/IDAACS58523.2023.10348948

Current Advances on Cloud-Based Distributed Computing for Forest Monitoring

Autori: Andrii Shelestov, Yevhenii Salii, Nataliia Hordiiko, Hanna Yailymova
Pubblicato in: ISBN 978-3-031-46880-3
Editore: Springer, Cham
DOI: 10.1007/978-3-031-46880-3_20

Peer reviewed articles (5)

DIAMANTE: A data-centric semantic segmentation approach to map tree dieback induced by bark beetle infestations via satellite images

Autori: Giuseppina Andresini, Annalisa Appice, Dino Ienco & Vito Recchia
Pubblicato in: Journal of Intelligent Information Systems, ISSN 1573-7675
Editore: Springer Nature
DOI: 10.1007/S10844-024-00877-6

Statistical methods of feature engineering for the problem of forest state classification using satellite data

Autori: Yevhenii Salii, Alla Lavreniuk, Nataliia Kussul
Pubblicato in: System Research & Information Technologies, ISSN 1681-6048
Editore: IASA
DOI: 10.20535/SRIT.2308-8893.2024.1.07

SILVIA: An eXplainable Framework to Map Bark Beetle Infestation in Sentinel-2 Images

Autori: Donato Malerba, Annalisa Appice, Giuseppina Andresini
Pubblicato in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, ISSN 1939-1404
Editore: Institute of Electrical and Electronics Engineers
DOI: 10.1109/JSTARS.2023.3312521

Conservation policies and management in the Ukrainian Emerald Network have maintained reforestation rate despite the war

Autori: Leonid Shumilo, Sergii Skakun, Meredith L. Gore, Andrii Shelestov, Nataliia Kussul, George Hurtt, Dmytro Karabchuk, Volodymyr Yarotskiy
Pubblicato in: Commun Earth Environ, ISSN 2662-4435
Editore: Springer Nature
DOI: 10.1038/S43247-023-01099-4

Google Earth Engine Framework for Satellite Data-Driven Wildfire Monitoring in Ukraine

Autori: Bohdan Yailymov, Andrii Shelestov, Hanna Yailymova, Leonid Shumilo
Pubblicato in: Fire, ISSN 2571-6255
Editore: MDPI
DOI: 10.3390/FIRE6110411

Monographic books (1)

Innovative approaches for forest monitoring using remote sensing and cloud computing

Autori: O. Fedorov
Pubblicato in: ISBN 978-966-360-507-4
Editore: PH “Akademperiodyka”
DOI: 10.15407/AKADEMPERIODYKA.507.172