Ephemeral gully recognition and accuracy evaluation using deep learning in the hilly and gully region of the Loess Plateau in China

Abstract

Ephemeral gullies are widely distributed in the hilly and gully region of the Loess Plateau and play a unique role in the slope gully erosion system. Rapid and accurate identification of ephemeral gullies impacts the distribution law and development trend of soil erosion on the Loess Plateau. Deep learning algorithms can quickly and accurately process large data samples that recognize ephemeral gullies from remote sensing images. Here, we investigated ephemeral gullies in the Zhoutungou watershed in the hilly and gully region of the Loess Plateau in China using satellite and unmanned aerial vehicle images and combined a deep learning image semantic segmentation model to realize automatic recognition and feature extraction. Using Accuracy, Precision, Recall, F1value, and AUC, we compared the ephemeral gully recognition results and accuracy evaluation of U-Net, R2U-Net, and SegNet image semantic segmentation models. The SegNet model was ranked first, followed by the R2U-Net and U-Net models, for ephemeral gully recognition in the hilly and gully region of the Loess Plateau. The ephemeral gully length and width between predicted and measured values had RMSE values of 6.78 m and 0.50 m, respectively, indicating that the model has an excellent recognition effect. This study identified a fast and accurate method for ephemeral gully recognition in the hilly and gully region of the Loess Plateau based on remote sensing images to provide an academic reference and practical guidance for soil erosion monitoring and slope and gully management in the Loess Plateau region.

Keywords

• Deep learning
• Remote sensing image
• Ephemeral gully recognition
• Loess plateau
• Image semantic segmentation
• Accuracy evaluation