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.

利用深度学习的最新进展，本文展示了如何利用YOLOv8神经网络来准确地分割航空LiDAR图像中的考古结构，通过对原始LiDAR数据的新型预处理和数据增强方法来训练YOLOv8网络，提高了平台和环状结构的分割的准确度、精度和召回率，优于现有方法，该方法自动化了耗时的LiDAR图像标注，显著加速了对历史景观的准确分析。

利用航空LiDAR图像分割揭示古代玛雅定居点