Broadscale deep learning model for archaeological feature detection across the Maya area

Abstract

Many Maya archaeological areas are not comprehensively or systematically mapped because ruins, often hidden under tropical forest canopy in rugged terrain, can take decades to locate, identify, and map. Recent years have seen an explosion of lidar data collection, and machine learning provides a way to exploit these lidar data, making feature analyses more efficient and consistently executed. At present, there are a limited number of small, area-specific models that exist for the Maya area, the largest of which covers 230 km². Here we present the foundation for a broadscale, multi-area-based convolutional neural network (CNN) object detection model that uses airborne laser scanning data, or lidar, for archaeological feature detection across 615 km² of the Maya area, as well as preliminary results from an additional 885 km² test area. This sets the path for a model that will enable researchers to map archaeological areas across the entire Maya Lowland area in weeks or months instead of decades. Notably, we find that a model trained on multiple areas with significantly different topographies produces better results for all areas as compared to a model trained on a single area. The broadscale model here presented produced an F1 score of 0.80. Results also include many potential new structure detections, including detections on lidar at an archaeological area that has not yet been comprehensively ground-surveyed and is located in an entirely different country in the Maya Lowlands from where the model was trained on. This model represents an attempt at a broadscale machine learning approach for archaeological feature mapping in the Maya area and demonstrates how big data can be integrated into traditional archaeological workflows. Lidar has already shown much greater ancient Maya infrastructure throughout the Maya world and elsewhere in the tropics, and this study using machine learning with lidar is showing even greater Maya infrastructure through vast areas of the Maya tropical forest.