Evaluating Broadscale Deep Learning for Maya Settlement Detection in G-LiHT Lidar

Abstract Examining Lidar data is an efficient way to detect ancient Maya features across the Yucatan Peninsula. Automated object detection powered by deep learning leverages Maya archaeologists’ specialist knowledge in detecting the presence of ancient Maya settlements. By using a broadscale approach in its training, our new efficient multi-regional model Q2000 achieves comparable performance across a significantly broader and more diverse geographic region. This study addresses the current limitation of small-scale, area-specific models to generalize characteristics and properly detect a diverse range of target objects over a large area. This study introduces the foundational development of a broadscale, multi-region convolutional neural network (CNN) object detection model utilizing Lidar data across a significantly larger extent of the Maya area (approximately 35,584 km 2 ). This model achieved accuracies comparable to previous local studies that relied on the annotation of a larger number of structures within smaller, more homogeneous areas. Comparative analysis of the model's test results indicates enhanced generalization across diverse topographic regions when trained on multi-area data, achieving a robust F1 Score of 0.89, even with a relatively small training sample set. Our results further indicate that a broadscale approach to deep learning is efficient, and that a pan-Yucatan model can be effective.