## 4 Experiment Design  
### Dataset  
The datasets used to train and evaluate the model come from the publicly available data repository of the ISPRS 2D Semantic Labeling Challenge [32], in the German city of Potsdam, and it is composed of high-resolution true-color orthophoto images and the corresponding normalized DSM data. The dataset also includes a smaller dataset on the German city of Vaihingen, but this part has not been included in our experiments. As it will be explained later in this paper, in terms of orthophots, the Vaihingen dataset contributes \begin{table}
\begin{tabular}{c c c c} \hline
**Model** & **Top-5 Acc** & **\#Params** & **\#FLOPs** \\ \hline EfficientNet-B0 [16] & 93.3\% & 5.3 M & 0.39 B \\ EfficientNet-B2 [16] & 94.9\% & 9.2 M & 1.0 B \\ EfficientNet-B4 [16] & 96.4\% & 19 M & 4.2 B \\ VGG-16 [30] & 91.9\% & 138 M & 19.6 B \\ ResNet-50 [28] & 93.0\% & 26 M & 4.1 B \\ SENet [21] & 96.2\% & 146 M & 42 B \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of image classification efficiency based on the ImageNet dataset [29]: EfficientNet models [16] versus classical models. false-color IRRG images only, whose radiometric behavior does not match what was learnt on RGB images by the pre-trained networks used in the proposed method. In the original dataset, each parcel of land was classified into six common land cover classes, and this classification is distributed as Ground Truth (GT) to support the supervised learning procedure. The problem addressed in this paper, i.e., basic building mapping, only uses two labels, namely \"building\" and \"non-building\". Therefore, binary thematic maps containing only the desired classes were created by merging previous classes into the two relevant ones using simple image processing techniques. In Figure 3, an example of an image patch with the corresponding binary thematic map is presented. The organizers of the Challenge also defined a partition of the dataset into training and testing images. Since our research involved a Deep Learning method and, consequently, the need for hyperparameter tuning, the dataset was split into three subsets: one for training, one for validation, and one for testing. The Potsdam dataset contains 38 images that were randomly assigned to one of the three subsets so that the training subset contained \(\approx\)80%, validation \(\approx\)10%, and test \(\approx\)10% of the original images. It is to be noted that visual inspection of orthophoto images revealed noticeable geometrical distortions in some places, as in the example of Figure 4.  
These are probably due to stitching of multiple images in the production phase, and such distortions are not reflected in the ground truth, thus creating a mismatch between optical data and reference. Although the phen  
Figure 4: Example of visible distortion in RGB input images. Figure 3: (**a**) RGB image patch. (**b**) DSM patch. (**c**) Corresponding binary thematic map. Building pixels are displayed in white, whereas non-building pixels are displayed in black. across the dataset, this must be taken into account in evaluating results as it can lead to a underestimation of the actual capability of the model in segmenting the input. The model was trained using a subsection of the Potsdam dataset. The True OrtoPhoto (TOP) in such dataset come as TIFF files in different channel compositions, namely IRRG, RGB, and RGBIR. Since the model was initialized with pre-trained EfficientNet-B0 weights tuned on RGB-coded images, the RGB version of the TOP images offered in the Potsdam dataset was used. On the other hand, the Vaihingen dataset provides only IRRG TOP images; because of this mismatch, only the Potsdam section of the ISPRS 2D Semantic Labeling Challenge was used to train, validate, and test the model.