Home – Home – Jornals – Avtometriya 2026 number 1

The article discusses algorithms and neural network models for semantic segmentation of the Amur River water surface areas obtained using aerial photography. A dataset has been prepared for training a neural network based on aerial photography materials of the Amur River water area. The article presents the results of research on the accuracy of prediction of the most popular models in the field of semantic segmentation, such as UNet++, DeepLabV++, FPNet, and SAM. The experiments used the IoU (Jaccard similarity measure) and Boundure IoU (object boundary segmentation accuracy assessment) metrics. Computational experiments were conducted to measure the accuracy of the trained models in order to select the optimal parameters. As a result, it was found that the UNet++ model has an advantage in terms of segmentation accuracy, with an average Boundure IoU score of > 0.9. The developed algorithms and trained neural network models can be used in river water surface monitoring systems based on orthophoto images to determine the boundaries of the coastal zone.