Yailymova, HannaYailymov, BohdanSalii, YevheniiKuzin, VolodymyrShelestov, AndriiKussul, Nataliia2025-01-022025-01-022024A multimodal dataset for forest damage detection and machine learning / Hanna Yailymova, Bohdan Yailymov, Yevhenii Salii, Volodymyr Kuzin, Andrii Shelestov, Nataliia Kussul // IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, 7-12 July, 2024, Athens, Greece. - Athens, 2024. - P. 2949-2953https://ela.kpi.ua/handle/123456789/71524Accurately recognizing areas of forest damage is crucial for planning, monitoring recovery processes, and evaluating environmental impact following catastrophic events. The widespread accessibility of satellite data, coupled with the ongoing advancement of machine and deep learning techniques, as well as computer vision methods, renders the implementation of these approaches in the automatic detection of damaged forest areas highly difficult. Nevertheless, a significant challenge in this regard is the scarcity of labeled data. The purpose of this article is to provide a useful and reliable dataset for territory of Ukraine for scientists, conservationists, foresters and other stakeholders involved in monitoring forest damage and its consequences for forest ecosystems and their services. The created dataset contains 18 locations with a time series of satellite images with a resolution of up to 10 m per pixel across Ukraine, as well as weather information. The data was collected from the Copernicus Sentinel-1,2 satellite missions as well as based on ERA-5 weather information.enForest damagesearth observationmachine learningdeep learningsemantic segmentationArticleP. 2949-2953https://doi.org/10.1109/IGARSS53475.2024.10641873