Satellites for Wilderness Inspection and Forest Threat Tracking

Informations projet

SWIFTT

N° de convention de subvention: 101082732

DOI

10.3030/101082732

Projet clôturé

Date de signature de la CE 18 Octobre 2022

Date de début 1 Novembre 2022

Date de fin 30 Avril 2026

Financé au titre de

• Digital, Industry and Space

Coût total

€ 3 680 231,26

Contribution de l’UE

€ 2 853 249,00

Coordonné par

AXA CLIMATE
France

CORDIS fournit des liens vers les livrables publics et les publications des projets HORIZON.

Les liens vers les livrables et les publications des projets du 7e PC, ainsi que les liens vers certains types de résultats spécifiques tels que les jeux de données et les logiciels, sont récupérés dynamiquement sur OpenAIRE .

Livrables

Other (1)

Connected web and mobile platform

Documents, reports (7)

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
Final report on synergies and collaboration

"Final report on synergies and collaborationPLEASE NOTE WE CANNOT INPUT MONTH 41 IN THE ""DUE DATE"" FIELD OF ""DELIVERABLE DETAILS"", BUT THIS IS THE END MONTH WE ENVISION WITH AN EXTENSION"

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

Publications

Conference proceedings (15)

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

Auteurs: Yevhenii Salii, Volodymyr Kuzin, Anton Hohol, Nataliia Kussul, Hanna Yailymova
Publié dans: ISBN 978-1-6654-6397-3
Éditeur: IEEE
DOI: 10.1109/EUROCON56442.2023.10199006

Deep Change Vector Analysis to Map Bark Beetle Outbreaks in Open Sentinel-2 Data

Auteurs: Giuseppina Andresini; Annalisa Appice; Donato Malerba; Vito Recchia
Publié dans: 2025 International Joint Conference on Neural Networks (IJCNN), 2025, ISSN 2161-4407
Éditeur: IEEE
DOI: 10.1109/IJCNN64981.2025.11229013

An Attention-Based CNN Approach to Detect Forest Tree Dieback Caused by Insect Outbreak in Sentinel-2 Images

Auteurs: Vito Recchia; Giuseppina Andresini; Annalisa Appice; Gianpietro Fontana; Donato Malerba
Publié dans: Lecture Notes in Computer Science, 2025
Éditeur: Springer Nature
DOI: 10.1007/978-3-031-78980-9_12

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

Auteurs: Yevhenii Salii; Volodymyr Kuzin; Alla Lavreniuk; Nataliia Kussul; Andrii Shelestov
Publié dans: ISSN 2153-7003
Éditeur: IEEE
DOI: 10.1109/IGARSS53475.2024.10642681

Satellite-Based Analysis of Forest Damage in Ukraine’s Protected Areas

Auteurs: S. Drozd, N. Kussul and A. Shelestov
Publié dans: 2026
Éditeur: IEEE
DOI: 10.1109/IDAACS68557.2025.11322406

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

Auteurs: Bohdan Yailymov; Hanna Yailymova; Andrii Shelestov; Leonid Shumilo
Publié dans: ISBN 979-8-3503-9611-9
Éditeur: IEEE
DOI: 10.1109/DESSERT61349.2023.10416520

Semi-Supervised European Forest Types Mapping using High-Fidelity Satellite Data.

Auteurs: Yailymov, Bohdan; Yailymova, Hanna; Kussul, Nataliia; Shelestov, Andrii
Publié dans: 2024

A multimodal dataset for forest damage detection and machine learning

Auteurs: Hanna Yailymova; Bohdan Yailymov; Yevhenii Salii; Volodymyr Kuzin; Nataliia Kussul; Andrii Shelestov
Publié dans: ISSN 2153-7003
Éditeur: IEEE
DOI: 10.1109/IGARSS53475.2024.10641873

Machine Learning for Ukraine's Forest Cover Damage Assessment based on Satellite Data

Auteurs: H. Yailymova, B. Yailymov, A. Shelestov and N. Kussul
Éditeur: IEEE
DOI: 10.1109/IDAACS68557.2025.11322046

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

Auteurs: Giuseppina Andresini, Annalisa Appice, Donato Malerba
Publié dans: ISBN 9798400702433
Éditeur: 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

Auteurs: Giuseppina Andresini; Annalisa Appice; Dino Ienco; Donato Malerba; Vito Recchia
Publié dans: ISSN 2153-7003
Éditeur: IEEE
DOI: 10.1109/IGARSS53475.2024.10641479

A Novel Approach for Rapid Detection of Forest Degradation and Diseases Through Anomaly Analysis of Sentinel-2 Spectral Data

Auteurs: S. Drozd, N. Kussul, H. Yailymova
Publié dans: 2025
Éditeur: Hochschule Anhalt
DOI: 10.25673/119219

GANDALF: A LLM-based approach to map bark beetle outbreaks in semantic stories of Sentinel-2 images

Auteurs: Vincenzo Pasquadibisceglie; Vito Recchia; Annalisa Appice; Donato Malerba; Giuseppe Fiameni
Publié dans: Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing, 2025
Éditeur: ACM
DOI: 10.1145/3672608.3707751

Semi-supervised forest type mapping in Europe on satellite data

Auteurs: Nataliia Kussul, Andrii Shelestov, Bohdan Yailymov, Hanna Yailymova
Publié dans: ISSN 2770-4254
Éditeur: IEEE
DOI: 10.1109/IDAACS58523.2023.10348948

Current Advances on Cloud-Based Distributed Computing for Forest Monitoring

Auteurs: Andrii Shelestov, Yevhenii Salii, Nataliia Hordiiko, Hanna Yailymova
Publié dans: ISBN 978-3-031-46880-3
Éditeur: Springer, Cham
DOI: 10.1007/978-3-031-46880-3_20

Peer reviewed articles (10)

A Deep Semantic Segmentation Approach to Map Forest Tree Dieback in Sentinel-2 Data

Auteurs: Giuseppina Andresini; Annalisa Appice; Donato Malerba
Publié dans: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Numéro 17, 2024, ISSN 2151-1535
Éditeur: IEEE
DOI: 10.1109/JSTARS.2024.3460981

ULISSE: Parameter-Efficient Adaptation of Earth Vision Models to Monitor Forest Disturbance in Sentinel-2 Timeseries

Auteurs: V. Recchia, G. Andresini, A. Appice, D. Ienco, G. Fiameni, D. Malerba
Publié dans: Ecological Informatics, Numéro 94, 2026, ISSN 1878-0512
Éditeur: Elsevier
DOI: 10.1016/J.ECOINF.2026.103668

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

Auteurs: Giuseppina Andresini, Annalisa Appice, Dino Ienco & Vito Recchia
Publié dans: Journal of Intelligent Information Systems, ISSN 1573-7675
Éditeur: Springer Nature
DOI: 10.1007/S10844-024-00877-6

Satellite information technologies for the creation of the Ukrainian segment of the international GEOSS system

Auteurs: O. Fedorov, N. Kussul, A. Shelestov, L. Kolos, L. Pidgorodetska, B. Yailymov, H. Yailymova, V. Prysiazhnyi, V. Moroz
Publié dans: Space Science and Technology, Numéro 3, 2025, ISSN 2518-1459
Éditeur: KHIT
DOI: 10.15407/KNIT2025.03.042

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

Auteurs: Yevhenii Salii, Alla Lavreniuk, Nataliia Kussul
Publié dans: System Research & Information Technologies, ISSN 1681-6048
Éditeur: IASA
DOI: 10.20535/SRIT.2308-8893.2024.1.07

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

Auteurs: Donato Malerba, Annalisa Appice, Giuseppina Andresini
Publié dans: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, ISSN 1939-1404
Éditeur: 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

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

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

Auteurs: Bohdan Yailymov, Andrii Shelestov, Hanna Yailymova, Leonid Shumilo
Publié dans: Fire, ISSN 2571-6255
Éditeur: MDPI
DOI: 10.3390/FIRE6110411

AI-Powered Digital Twin Framework for Land Use Change in Disaster Affected Regions

Auteurs: Nataliia Kussul, Gregory Giuliani, Andrii Shelestov, Anton Cherniatevych, Sofiia Drozd, Andrii Kolotii, Yevhenii Salii, Oleksandr Yavorskyi, Volodymyr Malyniak, Alla Lavreniuk, Charlotte Poussin
Publié dans: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Numéro 18, 2025, ISSN 1939-1404
Éditeur: Institute of Electrical and Electronics Engineers (IEEE)
DOI: 10.1109/JSTARS.2025.3623870

Forest Stress Detection Using Feature Engineering & Selection Approach Optimized for Satellite Imagery

Auteurs: Yevhenii Salii; Volodymyr Kuzin; Nataliia Kussul; Alla Lavreniuk
Publié dans: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Numéro 19, 2025, ISSN 2151-1535
Éditeur: IEEE
DOI: 10.1109/JSTARS.2025.3644488

Monographic books (1)

Innovative approaches for forest monitoring using remote sensing and cloud computing

Auteurs: O. Fedorov
Publié dans: ISBN 978-966-360-507-4
Éditeur: PH “Akademperiodyka”
DOI: 10.15407/AKADEMPERIODYKA.507.172