Semi-supervised European forest types mapping using high-fidelity satellite data

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dc.contributor.authorYailymov, Bohdan
dc.contributor.authorYailymova, Hanna
dc.contributor.authorKussul, Nataliia
dc.contributor.authorShelestov, Andrii
dc.date.accessioned2025-01-02T09:54:28Z
dc.date.available2025-01-02T09:54:28Z
dc.date.issued2024-09
dc.description.abstractAccurate and up-to-date forest type maps are crucial for effective monitoring and management of forest ecosystems across Europe. However, the availability of up to date high-resolution forest type maps has been limited. This study introduces an innovative semi-supervised approach for mapping European forest types by harnessing the power of high-resolution Sentinel-1 and Sentinel-2 satellite data from the Copernicus program. The novelty of the approach lies in the integration of various data sources for training dataset creation and the utilization of the Random Forest classifier on the Google Earth Engine cloud computing platform. This innovative combination enables efficient processing and classification of vast amounts of satellite imagery for large-scale forest type mapping. In particular, the LUCAS Copernicus 2018 and 2022 datasets were employed for training and validation, ensuring the robustness of the classification model. The resulting forest type map for 2022 has a fine spatial resolution of 10 meters and distinguishes between three key classes: broadleaved, coniferous, and mixed forests. Accuracy assessment using independent validation data demonstrated the reliability of the proposed approach, yielding an impressive overall accuracy of 93%. Comparative analysis with existing forest products revealed both consistencies and differences, underscoring the dynamic nature of forest ecosystems. The generated map fills a gap in up to date geospatial information on European forest types, empowering informed decision-making in forest management, conservation efforts, and environmental impact assessment. This study demonstrates the potential of synergizing cutting-edge remote sensing, cloud computing, and machine learning technologies to tackle complex environmental challenges at a continental scale, paving the way for future advancements in forest monitoring and management.
dc.description.sponsorshipHORIZON Europe project SWIFTT No. 101082732, “Satellites for Wilderness Inspection and Forest Threat Tracking.”
dc.format.pagerangeP. 1-15
dc.identifier.citationSemi-supervised European forest types mapping using high-fidelity satellite data / Bohdan Yailymov, Hanna Yailymova, Nataliia Kussul, Andrii Shelestov // ProfIT AI 2024: 4th International Workshop of IT-professionals on Artificial Intelligence (ProfIT AI 2024), September 25–27, 2024, Cambridge, MA, USA. - Cambridge, MA, 2024. - P. 1-15.
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/71497
dc.language.isoen
dc.publisherCEUR Workshop Proceedings (CEUR-WS.org)
dc.publisher.placeCambridge, MA, USA
dc.relation.ispartofProfIT AI 2024: 4th International Workshop of IT-professionals on Artificial Intelligence (ProfIT AI 2024), September 25–27, 2024, Cambridge, MA, USA
dc.subjectForest type classification
dc.subjectSentinel-1
dc.subjectSentinel-2
dc.subjectRandom Forest
dc.subjectGoogle Earth Engine
dc.subjectEurope1
dc.titleSemi-supervised European forest types mapping using high-fidelity satellite data
dc.typeArticle

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