# Recognition of Earthquake Surface Ruptures Using Deep Learning
## 4 Results and Analysis
The comparison results are shown in Figure 8, where it can be seen that our network can predict more effective crack information than the network without HDC and ASPP. We also drew the ROC curves of the two models in the maduoDom1 and maduoDom2 training sets  
Figure 6: Study area of the experimental data. and test sets, respectively, as shown in Figure 9. We can see that our network performance is better. Figure 7: Training process of the crack identification model by applying maduoDom1. Figure 8: Prediction results of different network architectures for maduoDom1 and maduoDom2. Figure 9: ROC curves of the models. Deep learning can accurately identify the crack areas in high-resolution UAV data. Nevertheless, small cracks remain difficult to identify. Therefore, we attempted to study whether the size of the crack area impacts the identification ability of the model. We marked the cracks with bold lines to increase the crack area and trained the deep learning model with new samples. For maduoDom1 and maduoDom2, the processing results using the bold-marked cracks are shown in Figure 10.  
First, we obtained 533 and 187 UAV high-resolution aerial images with bold labels in the maduoDom1 and maduoDom2 data, respectively. Then, we randomly selected 500 samples from the bold-labeled maduoDom1 data for training and 33 images for testing. We also randomly selected 160 samples for training and 27 images for testing in the bold-labeled maduoDom2 data. After the labels were changed to bold labels, the constructed semantic segmentation network was trained with the two processed datasets, and the optimal prediction model was obtained based on the loss value. The model converged faster during training with bold crack labels than without. After obtaining the optimal model, we performed crack identification and prediction for the two models in the two datasets. One of the identification results is shown in Figure 11. The recognition effect was significantly improved: both the crack position and the fitness were extremely accurate. Furthermore, the cracks predicted by the model were similar to the labels, indicating that the bold labeling of crack samples is conducive to improving the crack identification ability of the model. Similarly, we quantitatively evaluated the prediction effect of the two datasets (Table 2). The accuracy of the evaluation results in the test set exceeded 0.86. Compared with the original model trained with non-bold labels, the model trained with the bold crack labels displayed lower accuracy but better fitness of the cracks. In addition, the F1 and mIoU values in the test set were greatly improved because the crack area increases with the bold labels and the model can identify more cracks. Similarly, for the bold-labeled data, we also trained the model \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data** & **Accuracy** & **F1** & **mIoU** \\ \hline maduoDom1 with fine labels & 0.9817 & 0.5193 & 0.5031 \\ \hline maduoDom2 with fine labels & 0.9780 & 0.5211 & 0.5027 \\ \hline maduoDom1 with bold labels & 0.8644 & 0.7616 & 0.6412 \\ \hline maduoDom2 with bold labels & 0.8785 & 0.6326 & 0.5369 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative evaluation results of crack identification in the four datasets. Figure 10: Data processing with bold labels. that removed HDC and ASPP structures from the network we improved. The prediction results of the different networks are shown in Figure 11. Our network structure trained on bold-labeled data still has good crack recognition results.