## 4 Experiments
This figure shows that a large part of the vegetation was correctly labeledif we compare it with Figure 9a, so our method detected the structure pixels successfully. However, it is important to mention that the shadows in the images affected the classification of the pixels, since the structure pixels in shadow are similar to vegetation pixels in shadow. Therefore, providing images free of shadows to the classifier is advisable since it can help the classifier generate better classification results, producing better archaeological site detection. Figure 6: Training set for the manually labeled image. The red color represents structure, and the blue color represents non-structure. Figure 7: Fully manually labeled image corresponding to a structure of Calakmul. Using the binary image generated by the classifier, we segmented the archaeological site detected by our method in the RGB image by changing the color of the pixels labeled as non-structure to black. Thus, we isolate the archaeological structure from the vegetation. The obtained results can be seen in Figure 10b, where we can appreciate a large part of the structure was correctly segmented if we compare it with Figure 10a. Figure 11 shows the results obtained in the Palenque, Comalcalco, and Tikal archaeological sites, showing the images in RGB and the segmented RGB images. The archaeological sites of Palenque and Comalcalco obtained the best results since the quality of these images is better. The errors in Tikal's and Calakmul's images are due to the shadows and the soil's similarity with the archaeological structure. So it was not easy for the classifier to discriminate between structure and non-structure pixels. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Algorithm** & **Precision** & **Recall** & **F1 Score** & **Accuracy** \\ \hline \multicolumn{5}{c}{Calakmul}{} \\ \hline KNN & 1.00 & 1.00 & 1.00 & 1.00 \\ NN & 1.00 & 1.00 & 1.00 & 1.00 \\ RF & 1.00 & 1.00 & 1.00 & 1.00 \\ SVM & 1.00 & 1.00 & 1.00 & 1.00 \\ LR & 1.00 & 1.00 & 1.00 & 1.00 \\ \hline \multicolumn{5}{c}{Palenque} \\ \hline KNN & 1.00 & 0.99 & 0.99 & 0.99 \\ NN & 1.00 & 1.00 & 1.00 & 1.00 \\ RF & 1.00 & 1.00 & 1.00 & 1.00 \\ SVM & 1.00 & 0.96 & 0.98 & 0.98 \\ LR & 1.00 & 1.00 & 1.00 & 1.00 \\ \hline \multicolumn{5}{c}{Comalcalco} \\ \hline KNN & 1.00 & 0.97 & 0.98 & 0.99 \\ NN & 0.99 & 0.98 & 0.99 & 0.99 \\ RF & 1.00 & 0.99 & 0.99 & 0.99 \\ SVM & 0.98 & 1.00 & 0.99 & 0.99 \\ LR & 1.00 & 0.99 & 0.99 & 0.99 \\ \hline \multicolumn{5}{c}{Tikal} \\ \hline KNN & 1.00 & 1.00 & 1.00 & 1.00 \\ NN & 1.00 & 1.00 & 1.00 & 1.00 \\ RF & 1.00 & 1.00 & 1.00 & 1.00 \\ SVM & 1.00 & 1.00 & 1.00 & 1.00 \\ LR & 1.00 & 1.00 & 1.00 & 1.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results obtained for different classifiers performing five-fold cross-validation over the training set in each archaeological site. The results obtained for precision, recall, and F1 score correspond to the macro average. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Algorithm** & **Precision** & **Recall** & **F1 Score** & **Accuracy** \\ \hline KNN & **0.97** & 0.92 & 0.94 & 0.94 \\ NN & 0.95 & 0.94 & 0.94 & 0.96 \\ RF & 0.96 & 0.92 & 0.94 & 0.96 \\ SVM & 0.96 & **0.95** & **0.95** & **0.97** \\ LR & 0.96 & 0.94 & **0.95** & **0.97** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results obtained for the different classifiers over the whole image. The results obtained for precision, recall, and F1 score correspond to the macro average. The best results are highlighted in black. Figure 8: Results of changing the color space to HLS and applying the filters for the satellite image of Calakmul: (**a**) HLS; (**b**) Laplacian; (**c**) Canny; (**d**) Sobel. Figure 10: (**a**) Satellite image of Calakmul in RGB, and (**b**) structure detected by our method in RGB. Figure 9: Detection of the archaeological site of Calakmul. (**a**) Satellite image of Calakmul in RGB. (**b**) Structure detected by our method of the archaeological site.