  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.
