## 2 Related Work  
According to UNESCO, armed conflicts and war, earthquakes and other natural disasters, pollution, poaching, uncontrolled urbanization, and unchecked tourist development pose significant problems to world heritage sites [22; 23; 24; 25]. Due to this, records of archaeological structures have been generated worldwide through images. However, the amount of data makes analysis difficult. The development of artificial intelligence methods and approaches can help to facilitate the collection and understanding of the data in less time. Today, machine learning and computer vision techniques have assisted archaeologists in evaluating, predicting, and classifying their findings using images [26; 27].  
Literature shows different types of works that have used these techniques in the area of archaeology; for example, some works have been carried out to detect shipwrecks automatically through methods that use deep learning using elevation maps to apply new methodologies in the area of underwater archaeology, obtaining results that compete with or improve on those made by archaeologists [28]. Another area in which these methods have been used is the classification of ancient artifacts, such as vessels from a Bronze Age cemetery in Saxony. The results exceeded archaeologists' expectations by eliminating systematic errors within the typology [15]. Artificial neural networks have also been applied to attributing the origins of archaeological ceramics by analyzing their composition found in different groups of archaeological sites, showing promising results [17]. Another work focused on computer vision proposes a new representation of Mayan hieroglyphs that includes information from both the foreground and the background, obtaining better results than the state of the art [29]; in a later work, a proposal was also made to improve this representation using thinned hieroglyphs by introducing an improved selection of points of interest which significantly improved the results of hieroglyph recovery [30].  
On the other hand, the work carried out by [31] is based on monitoring and documenting archaeological sites in the Arctic using RGB images and thermal images obtained with the help of a drone. The results obtained by combining RGB and digital surface models helped to map the ruins and structures of the study sites. Similarly, [PERSON]. [32] focuses on identifying archaeological sites (tombs) in forested areas using time series of images with the help of machine learning. The results of this work indicate that the method works well for clustered tombs. Likewise, Ref. [33] uses machine learning techniques with satellite images to detect Roman fortified sites in arid environments, obtaining results with an overall accuracy of 0.93 with field study data. Finally, [PERSON] et al. [21] use orthomosaics from the Jergalan area to analyze funery complexes in Mongolia, using different machine learning algorithms to detect stone structures. The method uses different color spaces (RGB and HSV), textures (homogeneity, contrast, and entropy), and DEM maps and results in images that compete with or improve on the results obtained by archaeologists performing a manual classification. However, this method does not work well for jungle or wooded regions.  
The use of image analysis and supervised classification is a tool that could serve to support archaeological research in different areas, such as the detection of archaeological structures, even in sites with large amounts of vegetation. All of the above motivates us to propose a new method that allows the detection of ancient structures in areas with large amounts of vegetation, such as those in the Mesoamerican region, which is introduced in the next section.