Semantic Segmentation of the Amur River Areas in Orthophotoplan Image

The algorithms and neural network models for semantic segmentation of the Amur River water surface areas obtained using aerial photography have been considered. A data set has been prepared for training a neural network based on aerial photography materials of the Amur River water area. 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, have been presented. The IoU (Jaccard similarity measure) and Boundary IoU (object boundary segmentation accuracy assessment) metrics were used in the experiments. Computational experiments were conducted to measure the accuracy of the trained models 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 Boudary IoU score of >0.9 . The developed algorithms and trained network models can be used in river water surface monitoring systems based on orthophotoplan images to determine the boundaries of the coastal zone at various water levels.