2.3 Model selected
Liu et al. (2021) used six indexes (Accuracy, Precision, Recall,
F1 value, ROC curve, and AUC) to compare the gully recognition results
and accuracy evaluation of the U-Net, R2U-Net, and SegNet image semantic
segmentation models. The SegNet model ranked first for gully recognition
in the hilly and gully region of the Loess Plateau, followed by the
R2U-Net and U-Net models (Liu et al. , 2021). The gully length and
width between predicted and measured values had RMSE values of 6.78 m
and 0.50 m, respectively, using the SegNet model, indicating its
superior performance for gully recognition and morphological feature
extraction. Hence, this study used the SegNet model for gully
recognition and morphological feature extraction at the watershed scale.
Figure 2 is a network structure diagram of the SegNet model.