AUTOMATIC SEGMENTATION OF TREE CROWNS IN PINE FORESTS USING MASK R-CNN ON RGB IMAGERY FROM UAVS

This article presents the results of applying an improved method for automatic segmentation of RGB imagery (orthophotos) obtained using consumer-grade unmanned aerial vehicles (UAVs) based on the Mask R-CNN neural network. Preparation and post-processing blocks for raster and vector files were developed for geospatial data processing. The model was trained on 7,000 tree crowns identified in pine forest of drained habitats in the mixed coniferous-broadleaved forest subzone. Training was carried out using cross-validation. Additional data on 1,337 crowns were used for verification. Sequential filtering by area, score, and duplicate segments improved the quality of the final segmentation results for all age groups of pine forests. The model produced an average precision of 0.87, a recall of 0.81, and an F1-score of 0.83. The obtained results demonstrate the high efficiency of the filtering algorithm in reducing segment redundancy and increasing data reliability. The Mask R-CNN automatic segmentation method is an effective tool for studying the characteristics of pine forest stands based on RGB orthophotos obtained during UAV surveys; using this method, it is possible to reproduce the results of visual interpretation with high precision. This method is particularly effective for scaling studies to large areas, where manual interpretation would be labor-intensive.