## 4 Results
### Main Experimental Results
#### 4.4.3 Thinning Necessity
For instance, a dense old forest on less vigorous sites might appear similar to a dense middle-aged forest on productive sites or vice versa (Figure 6 row 6). Likewise, young forest on very viable sites might already require thinning during the planning period whereas a similar forest on less viable sites grows slower and should therefore not be planned for thinning. Unquestionably, the model produces misclassifications, yet the results are excellent for most cases. For example, it correctly classifies the recently thinned forest as not to be thinned (Figure 6 row 2). Similarly, the model has no problems classifying correctly the cut forest and the more sparsely standing forest as _no thinning_, whereas the dense forest it accurately predicts as _thinning_ (Figure 6 row 3 and 4). As foresters are particularly interested in the differentiation between forest with and without the necessity of thinning, another model was trained that discriminates just among the classes _thinning_ and _no thinning_. For this experiment, the dataset containing the ground truth _masked TN_ was used, which just contains information about commercial forests while all other data was masked. As reported in Table 8, a mean \(F_{1}\) score of 85.32% was obtained. Hence, by focusing on merely two classes, the mean \(F_{1}\) score was raised by 2.85% compared to the dataset without masking (Table 6). When concentrating on the class-specific scores, it can be seen that the mean \(F_{1}\) score's gain is due to the more accurate classification of the class _thinning_. More detailed information about the misclassifications between the classes _thinning_ and _no thinning_ is provided in the confusion matrix in Table 9. What is striking is the lower number of categorized pixels due to the masking of non-commercial forest areas and the much higher number of false positives compared to the false negatives. This finding contrasts with the first model evaluated (Table 7), where the false positives and the false negatives were relatively balanced. Precisely this behavioral distinction can be observed in the examples of Figure 6. Particularly apparent is the difference in sensitivity in the fifth row of Figure 6, where the prediction of the non-masked dataset (Figure 6 column d) predicts _no thinning_ on the entire tile while the prediction of the masked dataset (Figure 6 column f) classifies the entire commercial forest as in need of thinning. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Score** & **Thinning** & **No Thinning** & **Mean** \\ \hline
**precision** & 76.98\% & 92.27\% & 84.63\% \\
**recall** & 85.84\% & 86.82\% & 86.33\% \\
**IoU** & 68.31\% & 80.93\% & 74.62\% \\
**F1** & 81.17\% & 89.46\% & **85.32\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Classification scores and mean classification scores on the test set (ground truth _masked TN_). The model’s objective is to detect the necessity of thinning restricted to the commercial forest. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Prediction**} \\  & **Class** & **Thinning** & **No Thinning** & \(\Sigma\) \\ \hline \multirow{2}{*}{**Reference**} & **thinning** & 97 & 16 & 113 \\  & **no thinning** & 29 & 191 & 220 \\ \hline \multirow{2}{*}{} & \(\Sigma\) & 126 & 207 & 333 \\ \cline{2-5}  & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & & \\ \end{tabular}
\end{table} 
Table 9: Confusion matrix on test set (ground truth _masked TN_). The numbers represent classified pixels times \(10^{6}\).