## 4 Experiments  
To assess the proposed method, we applied it to images of four archaeological sites: Calakmul, Palenque, Comalcloo, and Tikal. For this, we manually selected small training sets of pixels, as explained in the previous section. Some images of the archaeological sites used in this experiment can be seen in Figure 5. To show the performance of our proposal, we performed three evaluations. First, we evaluated our method by applying five-fold cross-validation on the selected training set. As a second evaluation, we used the chosen training set on an image to classify all of the pixels in the image. Then, we assessed the classification by comparing the classifier's result against the fully labeled image. Finally, a third evaluation is presented to show the performance of our proposal in a more realistic scenario; for this, we used high-resolution satellite images and graphically show the archaeological structures detected by our proposed method. For our first evaluation, we selected training sets from four archaeological sites: Calakmul, Palenque, Comalcloo, and Tikal. For this, a training set was created for each archaeological site, manually selecting rectangles of different sizes on areas with structures and non-structures. Table 1 shows the sizes of the training sets for each archaeological site. Since the selection was made manually, the number of pixels selected for each image varies due to the size of the selected rectangles. The pixels selected for the Calakmul area can be seen in Figure 4.  
Figure 4: Image manually labeled. The red color represents structure samples, and the blue color represents non-structure samples. To evaluate the performance of our proposal, we used the training set and applied five-fold cross-validation over this set to obtain an evaluation of the classifiers. This process consists of creating five subsets called folds, each with 20% of the training set, each containing the same proportion of structure and non-structure pixels as the whole training set. Then, we took four folds to train the classifier and one fold to test the classifier and evaluate its classification quality. This process was repeated five times, taking each of the five folds as a test set. Finally, the value reached by the quality measures in each of the five evaluations was averaged and reported. Although semantic segmentation is a major part of our work, we decided not to use segmentation-based performance measures such as IoU and BF score since, in a real scenario, the images are not apriori whole segmented. Thus, the measures used to evaluate the classifier in our experiment were: precision, recall, F1 score, and accuracy, since they are widely used to evaluate the performance of a classifier. To compute these metrics, it was necessary to know the values TP (true positive = number pixels of structure correctly classified as structure), TN (true negative = number pixels of non-structure correctly classified as non-structure), FP (false positive = number pixels of non-structure wrongly classified as structure), and FN (false negative = number pixels of structure wrongly classified as non-structure), where the positive class corresponds to \begin{table}
\begin{tabular}{l c c} \hline
**Archaeological Site** & **Structure** & **Non-Structure** \\ \hline Calakmul & 178 & 222 \\ Palenque & 133 & 204 \\ Comalcalco & 193 & 564 \\ Tikal & 113 & 192 \\ \hline \end{tabular}
\end{table} 
Table 1: Training set size for the four archaeological sites. Figure 5: Archaeological sites used for the experiments. (**a**) Calakmul, Mexico. (**b**) Palenque, Mexico. (**c**) Comalcalco, Mexico. (**d**) Tikal, Guatem. structure pixels, and the negative class corresponds to non-structure pixels.