## 4 Results  
### Experimental Design  
The experimental design can be divided into three major parts and is illustrated in Figure 4. In the first part (Section 4.4.1), a search for the best-performing DCNN architecture is performed. Then, the results of the models predicting the necessity of thinning (Section 4.4.3) and the urgency of thinning (Section 4.4.4) are analyzed. Finally, an ablation study (Section 4.4.5) is performed to determine the impact of the various input data sources.  
The exploration to find the best-performing DCNN architecture was conducted exclusively on the dataset with the ground truth _TN_ (Table 3). The other ground truth types, with _TU_ used later to clarify if deep nets can determine thinning urgency, was not employed in searching for the best model. From the literature review reported in [62], the following three DCNNs were found as the most promising architectures for solving the semantic segmentation problem:  
* UNet [27];
* FC-DenseNet [34];
* DeepLabv3+ [38].  
Figure 3: Input data tile as used in the final dataset (non-masked). RGB: true color orthophoto, CIR: the color infrared orthophoto, CHM: crown height model, DTM: digital train model, Slope: slope and Ground Truth: ground truth _TN_. Ground truth classes are colored as follows: green represents forests not to be thinned (class: forest), red represents forest thinning is necessary (class: thinning), and blue represents everything else (class: other).  
The architecture utilized in this study is shown in Figure 11. The DCNN was modified to accept the tiles from the dataset with the dimensions 512 \(\times\) 512 \(\times\) 7 as input and to output the predictions as a 512 \(\times\) 512 \(\times\) 1 array. We searched for the optimal architecture by evaluating the DCNNs on the dataset (_TN_). The DCNN achieving the best result was then chosen for further optimization experiments. This optimization consists of manipulating individual parts of the architecture to further optimize the model's performance by applying the Bayesian optimization method [63]. This method is an alternative to an exhaustive full grid search, which is computationally expensive. Instead, it more efficiently searches for optimal hyper-parameters. In practice, we sequentially altered critical structures in the DCNN architecture and always adopted the structure that provided the best results. All DCNNs were implemented in PyTorch, and all supplementary code was written in Python. All code is freely available at [[https://github.com/satlawa/edin_thinning_necessity](https://github.com/satlawa/edin_thinning_necessity)]([https://github.com/satlawa/edin_thinning_necessity](https://github.com/satlawa/edin_thinning_necessity)) (accessed on 19 August 2023). The experiments were performed on a system with Ubuntu 20.04 as the operating system equipped with an I7 6700K CPU, 32 GB of RAM, and an Nvidia RTX 3090.  
After finding the best architecture, the final models were trained on the two datasets with all four ground truths (_TN_, _TU_, _masked TN_, and _masked TU_). These final models were then evaluated on the test set to estimate the actual performance on unseen data. Subsequently, the results were analyzed to determine whether the models could satisfactorily fulfill the intended task. In the case of the ground truths _TN_ and _masked TN_, we examined if the necessity of thinnings can be detected via a DCNN using solely remote sensing data. Furthermore, the ground truths _TU_ and _masked TU_ were used to investigate if the urgency of thinnings can be determined in addition to detecting the necessity of thinnings.  
Finally, an ablation study was performed by omitting different types of data from the input dataset with ground truth _TN_. The primary purpose of this experiment was to determine the importance of the individual input data types, as shown in Table 1.