Transfer Learning of Semantic Segmentation Methods for Identifying Buried Archaeological Structures on LiDAR Data
This work addresses data scarcity for archaeologists using LiDAR, but it is incremental as it builds on existing transfer learning methods without major breakthroughs.
The paper tackled the problem of limited training data for deep learning in archaeological remote sensing by comparing transfer learning configurations for semantic segmentation on LiDAR datasets, finding that transfer learning can improve performance but without systematic enhancement.
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.