The Segmentation of Unnamed Aerial Vehicle-Derived Aerial Photographs to Identify Anthropogenic Changes

The paper considers the issues of segmentation of aerial photographs obtained by Unnamed Aerial Vehicles to identify man-made changes.Neural networks are used for this purpose.Based on the orthophotoplan of the oil field area, a dataset was formed of about 4,500 tiles measuring 512 512 pixels with 18 classes of man-made zones marked.The images were filtered, balanced, and augmented, after which U-Net, DeepLabv3+, and SegFormer models were trained on them with the training, test, and validation sets divided into 70/15/15%.The best modification of U-Net showed an overall accuracy of 94.4% and mean Intersection over Union of 79.2%, while Intersection over Union values above 80% were obtained for key natural and man-made objects.DeepLabv3+ and SegFormer demonstrated comparable results (mean Intersection over Union about 74%) with better detail for large and rare classes.The proposed method ensures high accuracy and efficiency of analysis, which makes it promising for environmental monitoring.