## 4 Results
### Main Experimental Results
#### 4.4.5 Ablation Study  
An ablation study investigated the importance of individual input features by training models with various combinations of removed input features. The factors explored were the inclusion or exclusion of these data planes: orthophoto, CHM (crown height model), DTM (digital train model), and slope. The performance variations of the different models provide insight into what effect a specific input feature has on the model. See [62] for the full details of the experiments summarized below.  
The results showed that the inclusion of the DTM has little or a slightly negative impact on the model's performance. When examining the classification scores, it is primarily in the class _other_ where the inclusion of DTM deteriorates the performance. The input feature slope improves performance relative to models that do not contain it. Since slope is calculated from the DTM, we expected that the DCNN could at least partially learn to extract some useful information out of the DTM. However, it seems the DCNN is not capable of deriving slope out of the DTM. Possibly, this is because the input features slope and DTM provide only information about the terrain and not about the forest itself.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Score** & **Thinning 1** & **Thinning 2** & **No Thinning** & **Mean** \\ \hline
**precision** & 42.72\% & 50.32\% & 92.97\% & 62.00\% \\
**recall** & 64.74\% & 48.84\% & 83.19\% & 65.59\% \\
**IoU** & 34.66\% & 32.95\% & 78.27\% & 48.63\% \\
**F1** & 51.48\% & 49.57\% & 87.81\% & 62.95\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Classification scores and mean classification scores on the test set (ground truth _masked TU_). The model’s objective is to detect the urgency of thinnings restricted to the commercial forest.  
Omitting the input feature CHM results in models with a lower score compared with the equivalent models with CHM included, one can conclude that CHM contains some unique information since tree heights are essential criteria for assessing the necessity of thinning, as Section 2.1 briefly outlines.  
Figure 7: Examples of semantic segmentation on thinning urgency test set. Column (**a**) CIR are false color composites with near-infrared, (**b**) CHM is the canopy height model, (**c**) GT is the ground truth thinning urgency set _TUL_, (**d**) is the prediction of the model trained on the ground truth _TUL_, (**e**) GT-m is the ground truth _masked TU_, (**f**) is the prediction of the model trained on the ground truth _masked TU_. 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 1, yellow = thinning 2, green: no thinning, blue: other.  
Omitting the input feature orthophotos gives a significant decline in performance compared with the full model. It appears that orthophotos contains the most valuable information of all input features. This result coincides with the finding that the input feature orthophotos is also the most informative for human forest managers when assessing a forest.