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

A large part of the use of knowledge base systems is the interpretation of
the output by the end-users and the interaction with these users. Even during
the development process visualisations can be a great help to the developer. We
created IDPD3 as a library to visualise models of logic theories. IDPD3 is a
new version of $ID^{P}_{Draw}$ and adds support for visualised interactive
simulations.

介绍了 IDPD3 库，作为一种 visualisations 工具， 用于知识库系统中逻辑理论和交互式模拟的可视化和解释。