## 4 Experiments
To obtain these measures the following equations were used:  
\[Precision=\frac{TP}{TP+FP} \tag{1}\]  
\[Recall=\frac{TP}{TP+FN} \tag{2}\]  
\[F1-Score=2\cdot\frac{Precision\cdot Recall}{Precision+Recall} \tag{3}\]  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{4}\]  
Table 2 shows the results obtained with different metrics using different supervised classifiers: KNN (K-nearest neighbors, using Euclidean distance with a value of K = 5), NN (neural networks, with an Adam optimizer, a hidden layer size of (5, 5, 7), a learning rate of 0.001, an alpha value of 1 \(\times\) 10\({}^{-5}\), and an epsilon of 1 \(\times\) 10\({}^{-8}\)), RF (random forest, with a number of trees of 20 and a maximum depth of 3), SVM (support vector machines, using a polynomial kernel), and LR (logistic regression, with a liblinear optimizer and a maximum of 1000 iterations). In Table 2, we can see that most of the values are higher than 0.98, because the results were obtained based just on the pixels of the training set. The second evaluation uses a fully labeled image to assess the classification of unlabeled pixels in our proposed method. Thus, we select a small training set, as explained in the previous section. The pixels in the whole image are labeled by applying our method, and, using the same metrics, the classifier's quality is evaluated. These metrics were obtained by comparing the pixels labeled by the classifier against the correct label in the fully manually labeled image. The image used for this experiment corresponds to a small portion of the high-resolution image of the archaeological site of Calakmul. It contains an image with a size of 599 \(\times\) 512 pixels. The pixels selected for the training set in this image were 142 for structure and 256 for non-structure. Figure 6 shows this small image and the pixels selected for the training set. Figure 7 shows the fully labeled image, where the yellow outline delimits the areas corresponding to the archaeological structure. We manually labeled this image. The results obtained can be seen in Table 3, where we can see that these results are lower than those obtained in the previous experiment. This happens because the classifier wrongly labeled some pixels as non-structure compared to the fully labeled image. The labeling error is due to the shadows contained in the structures. However, these results are fairly good, from 92% to 97%. We show how our proposed method works in practice as a third evaluation. For this, we use large images from each of the archaeological sites. As an example, we only show the results obtained using our method on the image of the Calakmul site. For the other sites, we only show the image of the detected archaeological site output by our method. We selected Calakmul to show all the steps of our method because large amounts of vegetation hide it. However, the images of all the steps corresponding to the other archaeological sites and their results can be seen and downloaded from ([[https://ccc.inaoep.mx/~ariel/Detection-of-archaeological-structures/](https://ccc.inaoep.mx/~ariel/Detection-of-archaeological-structures/)]([https://ccc.inaoep.mx/~ariel/Detection-of-archaeological-structures/](https://ccc.inaoep.mx/~ariel/Detection-of-archaeological-structures/)), accessed on 14 May 2023). For the Calakmul site, the image in the HSL color space, as well as the filters (Laplacian, Canny, and Sobel), were generated and can be seen in Figure 8. In this figure, we can notice, especially in the HSL image, that a large part of the vegetation can be differentiated, which is a good result of our method. However, this does not always happen with all images. On the other hand, it can be seen that the Canny and Laplacian filters can better represent the vegetation, and the Sobel filter can emphasize the edges of the structure. Since all of the classifiers obtained similar results, we only show the results obtained with the neural network (NN) classifier. Figure 9b shows the binary image generated by this classifier.