  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.
