Transfer Learning of Semantic Segmentation Methods for Identifying Buried Archaeological Structures on LiDAR Data

arXiv (Cornell University)

Abstract

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.

Authors 5

1. Paolo Soleni

   Center for Cultural Heritage Technology

   Affiliation as printed

   Center for Cultural Heritage Technology Istituto Italiano di Tecnologia Venice , Italy

2. Wouter Verschoof‐van der Vaart Aachen

   Leiden University

   Affiliation as printed

   Leiden University Faculty of Archaeology Leiden , Netherlands

3. Žiga Kokalj
4. Arianna Traviglia

   Center for Cultural Heritage Technology

   Affiliation as printed

   Center for Cultural Heritage Technology Istituto Italiano di Tecnologia Venice , Italy

5. Marco Fiorucci

   Center for Cultural Heritage Technology

   Affiliation as printed

   Center for Cultural Heritage Technology Istituto Italiano di Tecnologia Venice , Italy Kyoto University Kyoto , Japan