Unveiling Ancient Maya Settlements Using Aerial LiDAR Image Segmentation

Manual identification of archaeological features in LiDAR imagery is labor-intensive, costly, and requires archaeological expertise. This paper shows how recent advancements in deep learning (DL) present efficient solutions for accurately segmenting archaeological structures in aerial LiDAR images using the YOLOv8 neural network. The proposed approach uses novel pre-processing of the raw LiDAR data and dataset augmentation methods to produce trained YOLOv8 networks to improve accuracy, precision, and recall for the segmentation of two important Maya structure types: annular structures and platforms. The results show an IoU performance of 0.842 for platforms and 0.809 for annular structures which outperform existing approaches. Further, analysis via domain experts considers the topological consistency of segmented regions and performance vs. area providing important insights. The approach automates time-consuming LiDAR image labeling which significantly accelerates accurate analysis of historical landscapes

Exploring Causal Path Directionality for a Marketing Model Using Cohen’s Path Method 1.

Researchers must frequently consider the directionality of relationships between variables when linking variables as well as when positing construct-to-construct relationships or when relations are specified at a higher order level of abstraction (Wilson, Callaghan & Stainforth, 2006). The psychometric literatures have been particularly mindful of these path directionalit

Settlement Scaling in the Northern Maya Lowlands: Human-Scale Implications

Settlement scaling theory predicts that higher site densities lead to increased social interactions that, in turn, boost productivity. The scaling relationship between population and land area holds for several ancient societies, but as demonstrated by the sample of 48 sites in this study, it does not hold for the Northern Maya Lowlands. Removing smaller sites from the sample brings the results closer to scaling expectations. We argue that applications of scaling theory benefit by considering social interaction as a product not only of proximity but also of daily life and spatial layouts. Investigadores de relaciones de escala en asentamientos predicen que densidades altas resultan en el aumento de interacciones social, lo cual estimula productividad. Relaciones de escala entre población y área de asentamiento se manifiestan para varias sociedades antiguas pero, como se ve en nuestra muestra de 48 sitios, no se manifiestan para el norte de la Península de Yucatán. Quitando sitios pequeños produce resultados más semejantes a las expectativas de escala. Aplicaciones de relaciones de escala tienen que considerar interacciones sociales como producto no solamente de proximidad sino de la vida cotidiana y patrones de espacio.</p