## 4 Results
### Main Experimental Results
#### 4.4.3 Thinning Necessity  
Based on the network architecture from Section 4.4.1, the model was evaluated on the _TN_ test set (Table 6). With a mean \(F_{1}\) score of 82.23%, the model achieves a similar score to the 83.01% on the _TN_ validation set. Thus, one can conclude that the chosen model is not overfitting the validation set. Additionally, when focusing on the class-specific scores, the model performs best on predicting the class _no thinning_, whereas the scores of the other two classes are significantly lower. When examining the confusion matrix in Table 7, one can gain further insight into the misclassifications of the model. Accordingly, the main mistakes are those between the classes _thinning_ and _no thinning_ as well as those between _no thinning_ and _other_, while misclassifications between the classes _thinning_ and _other_ are insignificant. These results match our knowledge about the classes. Examples of predictions are illustrated in Figure 6. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Score** & **Thinning** & **No Thinning** & **Other** & **Mean** & **Std** \\ \hline
**Precision** & 77.08\% & 91.58\% & 79.69\% & 82.78\% & 0.78\% \\
**Recall** & 80.32\% & 90.92\% & 74.15\% & 81.79\% & 0.52\% \\
**IoU** & 64.81\% & 83.89\% & 62.34\% & 70.35\% & 0.44\% \\
**F1** & 78.64\% & 91.24\% & 76.80\% & **82.23\%** & **0.31\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification scores and mean classification scores on the test set (ground truth _TN_) with five-fold cross-validation. The model’s objective is to detect the necessity of thinning. Classification definitions, _thinning_: thinning within 1–10 years, _no thinning_: no thinning necessary, _other_: non-forest areas. std: standard deviation. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & & \multicolumn{4}{c}{**Prediction**} \\  & **Class** & **Thinning** & **No Thinning** & **Other** & \(\sum\) \\ \hline \multirow{3}{*}{**Reference**} & **thinning** & 89 & 26 & 1 & 116 \\  & **no thinning** & 22 & 341 & 5 & 368 \\  & **other** & 1 & 7 & 24 & 32 \\ \hline \hline  & \(\sum\) & 112 & 374 & 30 & 516 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Confusion matrix on the test set (ground truth _TN_). The numbers represent classified pixel times \(10^{6}\). For instance, class _thinning_ represents dense forest with a minimum top height of around 13 m and thus contrasts to class _other_ that often represents no vegetation or shallow growing vegetation like grassland or mountain pines. In contrast, it makes sense to see the model misclassifying the class _no thinning_ with both other classes as it embodies forest at all ages. For example, a very young forest has almost identical features compared to grassland,  
Figure 6: Examples of semantic segmentation on the thinning necessity experiment test set. Columns: (**a**) CIR is false color composites with near-infrared, (**b**) CHM is the canopy height model, (**c**) GT is the ground truth of the thinning necessity set \(T\!N\), (**d**) is the prediction of the model trained on the ground truth \(T\!N\), (**e**) GT-m is the ground truth _masked_\(T\!N\), and (**f**) is the prediction of the model trained on the ground truth _masked_\(T\!N\). In (**b**), the color palette illustrates low heights as dark (dark blue) and high heights as bright (yellow). The colored pixels in (**c–f**) represent the following classes: black, void; red, thinning; green, no thinning; and blue, other. as presented in Figure 6 row 1. Therefore, it is challenging and sometimes impossible to distinguish between _no thinning_ and _other_ with only remote sensing data. Equally ambitious are some classification cases between the classes _thinning_ and _no thinning_. In this situation, the model struggles to differentiate between edge cases of dense forest (Figure 6 rows 5 to 8). These errors might arise due to a lack of information about the yield class of the forest.