Impact of LiDAR visualisations on semantic segmentation of archaeological objects

arXiv:2404.05512v1 Announce Type: new
Abstract: 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 …

abstract arxiv cs.cv deep learning deep learning performance digital framework images impact lidar objects paper performance research segmentation semantic study testing through type