05 September 2024


Accurately 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.
In this work, SWIFTT partners from the Space Research Institute of Ukraine provide a useful and reliable dataset of Ukraine’s territory for scientists, conservationists, foresters and other stakeholders involved in monitoring forest damage and its consequences for forest ecosystems and their services. The dataset contains 18 locations with a time series of satellite images with resolution up to 10 m per pixel across the country, as well as weather information. The data was collected from Copernicus’s Sentinel-1,2 satellite missions as well as based on ERA-5 weather information.
Read the paper in the link below.