Transfer Learning of Semantic Segmentation Methods for Identifying Buried Archaeological Structures on LiDAR Data
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.
- Simon H. Bickler, “Machine learning arrives in archaeology,” Advances in Archaeological Practice, 2021.
- “Machine learning for cultural heritage: A survey,” Pattern Recognition Letters, 2020.
- “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009.
- Wouter Verschoof-van der Vaart, Learning to look at LiDAR: combining CNN-based object detection and GIS for archaeological prospection in remotely-sensed data, Ph.D. thesis, Leiden University, 2022.
- Rafael Pires de Lima and Kurt Marfurt, “Convolutional neural network for remote-sensing scene classification: Transfer learning analysis,” Remote Sensing, vol. 12, 2020.
- “Bringing lunar lidar back down to earth: Mapping our industrial heritage through deep transfer learning,” Remote Sensing, vol. 11, 2019.
- “Learning to classify structures in als-derived visualizations of ancient maya settlements with cnn,” Remote Sensing, 07 2020.
- “Deep learning for archaeological object detection on lidar: New evaluation measures and insights,” Remote Sensing, 03 2022.
- “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” in 2015 IEEE International Conference on Computer Vision (ICCV), 2015.
- “U-net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, Nassir Navab, Joachim Hornegger, William M. Wells, and Alejandro F. Frangi, Eds. 2015, Springer International Publishing.
- “Encoder-decoder with atrous separable convolution for semantic image segmentation,” in Computer Vision – ECCV 2018: 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part VII. 2018, Springer-Verlag.
- “Deep residual learning for image recognition,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
- “EfficientNet: Rethinking model scaling for convolutional neural networks,” in Proceedings of the 36th International Conference on Machine Learning, Kamalika Chaudhuri and Ruslan Salakhutdinov, Eds. 09–15 Jun 2019, PMLR.
- “Segformer: Simple and efficient design for semantic segmentation with transformers,” in Advances in Neural Information Processing Systems, M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan, Eds. 2021, vol. 34, Curran Associates, Inc.
- “Tune: A research platform for distributed model selection and training,” 2018.
- “A system for massively parallel hyperparameter tuning,” in Proceedings of Machine Learning and Systems, I. Dhillon, D. Papailiopoulos, and V. Sze, Eds., 2020, vol. 2.
- “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014.
- “Objective comparison of relief visualization techniques with deep cnn for archaeology,” Journal of Archaeological Science: Reports, 08 2021.
- Wouter Verschoof-van der Vaart and Juergen Landauer, “Testing the transferability of carcassonnet. a case study to detect hollow roads in germany and slovenia,” in Proceedings of the 25th Conference on Cultural Heritage and New Technologies, CHNT25. 2022, Probylaeum, Heidelberg University Library.
- “Aerial bombing crater identification: Exploitation of precise digital terrain models,” ISPRS International Journal of Geo-Information, 2020.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.