Automated Generation of Tree Crown Images Dataset for Neural Network Training using UAV-Acquired Imagery
DOI: 10.21293/1818-0442-2025-28-2-130-136
DOI: 10.21293/1818-0442-2025-28-2-130-136
Abstract: Tree mapping is an important type of information demanded in various fields of research and practice. However, it is an expensive and time-consuming process, which makes it difficult to monitor large areas. Therefore, automated methods are necessary to optimize tree mapping in forest areas. This article proposes a software tool based on a dataset of tree crown images acquired using unmanned aerial vehicles (UAV), necessary for training neural networks. The basis of the accumulated dataset consists of tree crowns in high spatial resolution (1–10 cm) RGB images. The current dataset contains coniferous tree crown images acquired from different heights. To prepare this dataset, a method for extracting the crowns of each tree was applied in a semi-automatic image annotation program. The proposed tool can be successfully used for training neural networks and other machine learning approaches as well as computer vision techniques.
Keywords: Unmanned Aerial Vehicle (UAV), RGB, tree, crowns, image segmentation
For citation:
Kataev M. Yu., Kartashov E. Yu., Lukyanov A. K., Skvortsov Ya. O., Shurygin Yu. A., Kataev-Mihail-Yurievich M. Yu. Automated Generation of Tree Crown Images Dataset for Neural Network Training using UAV-Acquired Imagery. Doklady Tomskogo gosudarstvennogo universiteta sistem upravleniya i radioelektroniki, 2025, vol. 28, no. 2, pp. 130–136. DOI: 10.21293/1818-0442-2025-28-2-130-136
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Viktor N. Maslennikov
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