Deep learning methods in LiDAR-based archaeological research often leverage
visualisation techniques derived from Digital Elevation Models to enhance
characteristics of archaeological objects present in the images. This paper
investigates the impact of visualisations on deep learning performance through
a comprehensive testing framework. The study involves the use of eight semantic
segmentation models to evaluate seven diverse visualisations across two study
areas, encompassing five archaeological classes. Experimental results reveal
that the choice of appropriate visualisations can influence performance by up
to 8%. Yet, pinpointing one visualisation that outperforms the others in
segmenting all archaeological classes proves challenging. The observed
performance variation, reaching up to 25% across different model
configurations, underscores the importance of thoughtfully selecting model
configurations and LiDAR visualisations for successfully segmenting
archaeological objects.

本研究通过一个全面的测试框架，研究了可视化对深度学习性能的影响，结果表明适当的可视化选择可以提高性能高达 8%，然而找到一个在分割所有考古类别中表现出色的可视化方法仍然具有挑战性，实验结果突显了在成功分割考古对象时，精心选择模型配置和 LiDAR 可视化的重要性。

激光雷达可视化对考古物体语义分割的影响

Impact of LiDAR visualisations on semantic segmentation of  archaeological objects

When applying deep learning to remote sensing data in archaeological
research, a notable obstacle is the limited availability of suitable datasets
for training models. The application of transfer learning is frequently
employed to mitigate this drawback. However, there is still a need to explore
its effectiveness when applied across different archaeological datasets. This
paper compares the performance of various transfer learning configurations
using two semantic segmentation deep neural networks on two LiDAR datasets. The
experimental results indicate that transfer learning-based approaches in
archaeology can lead to performance improvements, although a systematic
enhancement has not yet been observed. We provide specific insights about the
validity of such techniques that can serve as a baseline for future works.

应用深度学习于考古学的遥感数据时，一个显著的障碍是适用于训练模型的数据集有限。本文通过比较两个 LiDAR 数据集上两种语义分割深度神经网络的多种迁移学习配置的性能，结论表明考古学中基于迁移学习的方法可以提高性能，但尚未观察到系统性的改进，为未来工作提供了基线。