Segmentation algorithm of <i>Ochotona curzoniae</i>-induced bare patches in alpine meadow based on deep lear-ning.

Accurate identification of bare patches caused by <i>Ochotona curzoniae</i> disturbance is fundamental for scientifically assessing the damage level. Traditional methods for recognizing and calculating the area of bare patches are often computationally complex and inefficient. Here, we proposed a wavelet-enhanced U-shaped convolutional neural network (W-UNet) segmentation method based on deep learning for unmanned aerial vehicle (UAV) imagery segmentation, which was based on the U-shaped convolutional neural network (UNet) architecture and used the 16-layer Visual Geometry Group network (VGG16) as the backbone. We introduced the coordinate attention mecha-nism (CA) in the skip connection section to enhance the spatial localization of target regions, and wavelet transform convolution (WTConv) during the encoding stage to improve high-frequency information extraction and the recovery of fine-grained features. Additionally, we employed a composite loss function combining Focal Loss and Dice Loss to effectively address the class imbalance issues. The results showed that the proposed method achieved a mean intersection over union (MIoU) of 81.2%, mean pixel accuracy (MPA) of 89.4%, and overall accuracy (ACC) of 95.8%, significantly outperforming the conventional UNet-Vgg model. This study would provide a robust technical framework for the efficient and accurate monitoring of bare patches induced by <i>O. curzoniae</i> infestation.