The forest constitutes an essential and irreplaceable component of life for all organisms, with its primary significance lying in its role in creating a breathable atmosphere on Earth. Forests are vital for human health and well-being and hold significant ecological and economic value for humanity. This study aims to propose a method for identifying forest stands using artificial intelligence techniques. A custom dataset was developed, comprising high-quality satellite images that capture various structures such as forests, fields, roads, buildings, and lakes. This dataset was employed to train models from the category of convolutional neural networks that operate on the principle of instance segmentation. Several models, including YOLOv8, YOLOv5 and Mask R-CNN, were tested and compared. An optimal model was selected based on parameters such as detection accuracy, total training time, and the precision of labeling detected image elements. The selected model was then evaluated using images not included in the original training dataset to simulate real-world deployment scenarios. The final accuracy of best model achieved 91.67%. This model can detect the presence of forest stands in satellite images, as well as other features such as roads, buildings etc. The proposed method offers potential benefits for forest technicians, who can integrate it with other methods to monitor forest cover effectively.
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