## 3 Methods for Landslide Identification
### Verification Method
#### 3.4.2 Precision Analysis  
The model must first have the premise of accurate recognition of the sample landslide to ensure the reliability of the final recognition results. In order to quantitatively evaluate the results of landslide recognition, this paper uses the commonly used indicators in the semantic segmentation problem to interpret the results. The confusion matrix is a basic and widely used approach [49] and it was built as shown in Table 3. When calculating the confusion matrix, the research uses the test set data for verification. The test set data are unknown to the trained model, which helps to objectively evaluate the model's learning of landslide characteristics. In the test set, landslide samples are treated as positive samples, and non-landslide samples are treated as negative samples.  
TP, FN, FP, and TN represent the quantity of predicted results of the model divided by threshold values in four different scenarios. Using these values, the following indicators can be calculated:  
Precision is used to measure the ratio of the correct target to the judged correct target, that is, the proportion of correct (true label positive) samples in all samples judged positive by the model. The calculation formula is as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{7}\]  
Recall is a metric that quantifies the percentage of correctly identified positive instances out of all actual positive instances. It essentially measures the ability of a model to capture all relevant instances in the dataset.  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{8}\]  
The F1-score represents the weighted harmonic mean of precision and recall, effectively balancing the influence of both metrics. This measure provides a more holistic view of a model's performance by simultaneously considering both the precision (the accuracy of positive predictions) and the recall (the model's ability to identify all relevant instances). A higher F1-score indicates superior performance of the model, showcasing its effectiveness in accurately and comprehensively identifying positive instances.  
\[F1-\text{score}=\frac{2\times\text{R}\times\text{P}}{\text{R}+\text{P}}=\frac {2\times\text{TP}}{2\times\text{TP}+\text{FP}+\text{FN}} \tag{9}\]  
The Kappa coefficient serves as a measure to validate the agreement between model predictions and actual outcomes. In the context of classification problems, this agreement  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Confusion Matrix**} & \multicolumn{3}{c}{**Predicted Value**} \\ \cline{2-4}  & & **Positive Example (+)** & **Counter Example (–)** \\ \hline \multirow{3}{*}{True value} & \multirow{3}{*}{Positive example (+)} & True Positive & False Negative \\  & & TP & FN \\ \cline{1-1}  & Counter example (–) & False Positive & True Negative \\ \cline{1-1}  & & FP & TN \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion Matrix.  
pertains to the consistency between the results derived from model assessments and the true classification outcomes.  
\[Kappa=\frac{N\sum\limits_{i=1}^{n}X_{ii}-\sum\limits_{i=1}^{n}(X_{i+}X_{+i})}{N^ {2}-\sum\limits_{i=1}^{n}(X_{i+}X_{+i})} \tag{10}\]  
where n is the sum of the number of rows in the confusion matrix (the total number of categories); \(X_{ii}\) is the number of samples in \(i\) row and \(i\) column in the confusion matrix, that is, the number of samples correctly classified; \(X_{i+}\) and \(X_{+i}\) are the total number of samples in row i and column i, respectively; N is the total number of samples used for accuracy evaluation. Mean intersection over union (MIoU) is the standard evaluation index of semantic segmentation networks and is the mean of the sum of the ratios of intersection and union between the predicted results of each category of the model and the real labels.  
\[M\text{IoU}=\frac{\text{TP}}{\text{FP+TP+FN}}+\frac{\text{TN}}{\text{FP+TN+FN}} \tag{11}\]