  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.
