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

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dc.contributor.authorSalii, Yevhenii
dc.contributor.authorKuzin, Volodymyr
dc.contributor.authorHohol, Anton
dc.contributor.authorKussul, Nataliia
dc.contributor.authorYailymova, Hanna
dc.date.accessioned2023-11-08T07:57:55Z
dc.date.available2023-11-08T07:57:55Z
dc.date.issued2023
dc.description.abstractThe paper deals with the problem of semantic segmentation of satellite imagery to deliver forest type map with high resolution. To solve the problem, we propose 4 machine learning models. Two of them are based on Random Forest (RF) and other two - on Convolutional Neural Network (CNN) - U-Net. As an input we use 2 images of Sentinel-2 (one for summer and one for winter, 4 spectral bands from each). As an output (labels) we use the Copernicus Forest Type dataset for 2018 year. Our models showed promising results on validation data. Of all models the one based on U-Net ended up being the most efficient in forest classification with overall accuracy 91.7%. At the same time the best RF model scored with 86.5%. After comparing the results, in order to check our model transferability we created and compared forest map of northern part of Kyiv region of 2018 and 2022. The experiment confirmed the robustness of the model and it's scalability. The developed models have been implemented in the cloud platform specialized on satellite data - CREODIAS. The developed map can provide valuable data for foresters, biologists, or other researchers to make decisions about forest management and conservation, as well as to ensure that Europe's forests are managed in an ecologically sustainable way.uk
dc.format.pagerangePp. 93-98uk
dc.identifier.citationMachine Learning Models and Technology for Classification of Forest on Satellite Data / Yevhenii Salii, Volodymyr Kuzin, Anton Hohol, Nataliia Kussul, Hanna Yailymova // In IEEE EUROCON 2023-20th International Conference on Smart Technologies. – 2023. – Pp. 93-98. – Bibliogr.: 34 ref.uk
dc.identifier.doihttps://doi.org/10.1109/EUROCON56442.2023.10199006
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/62058
dc.language.isoenuk
dc.relation.ispartofProceedings of IEEE EUROCON 2023uk
dc.subjectmachine learninguk
dc.subjectforest type classificationuk
dc.subjectSentinel-2uk
dc.subjectRandom Forestuk
dc.subjectConvolutional Neural Networkuk
dc.subjectU-Netuk
dc.titleMachine learning models and technology for classification of forest on satellite datauk
dc.typeArticleuk

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