Details
| Original language | English |
|---|---|
| Pages (from-to) | 155-173 |
| Number of pages | 19 |
| Journal | Journal of Computer Applications in Archaeology |
| Volume | 6 |
| Issue number | 1 |
| Early online date | 23 Nov 2023 |
| Publication status | Published - 2023 |
Abstract
Deep learning models need a lot of labeled data to work well. In this study, we use a Self-Supervised Learning (SSL) method for semantic segmentation of archaeological monuments in Digital Terrain Models (DTMs). This method first uses unlabeled data to pretrain a model (pretext task), and then fine-tunes it with a small labeled dataset (downstream task). We use unlabeled DTMs and Relief Visualizations (RVs) to train an encoder-decoder and a Generative Adversarial Network (GAN) in the pretext task and an annotated DTM dataset to fine-tune a semantic segmentation model in the downstream task. Experiments indicate that this approach produces better results than training from scratch or using models pretrained on image data like ImageNet. The code and pretrained weights for the encoder-decoder and the GAN models are made available on Github.1
Keywords
- Archaeology, Convolutional Neural Networks, Deep Learning, Digital Terrain Models, Generative Adversarial Networks, Relief Visualization, Self-Supervised Learning
ASJC Scopus subject areas
- Arts and Humanities(all)
- Archaeology
- Social Sciences(all)
- Archaeology
- Computer Science(all)
- Computer Science Applications
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In: Journal of Computer Applications in Archaeology, Vol. 6, No. 1, 2023, p. 155-173.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Self-Supervised Learning for Semantic Segmentation of Archaeological Monuments in DTMs
AU - Kazimi, Bashir
AU - Sester, Monika
N1 - Funding Information: This research was funded by the Lower Saxony Ministry of Science and Culture through the “Niedersächsisches Vorab” funding initiative and with the collaboration of Lower Saxony State Office for Heritage (Niedersächsisches Landesamt für Denkmalpflege). The publication of this article was funded by the Open Access Fund of the Leibniz Universität Hannover.
PY - 2023
Y1 - 2023
N2 - Deep learning models need a lot of labeled data to work well. In this study, we use a Self-Supervised Learning (SSL) method for semantic segmentation of archaeological monuments in Digital Terrain Models (DTMs). This method first uses unlabeled data to pretrain a model (pretext task), and then fine-tunes it with a small labeled dataset (downstream task). We use unlabeled DTMs and Relief Visualizations (RVs) to train an encoder-decoder and a Generative Adversarial Network (GAN) in the pretext task and an annotated DTM dataset to fine-tune a semantic segmentation model in the downstream task. Experiments indicate that this approach produces better results than training from scratch or using models pretrained on image data like ImageNet. The code and pretrained weights for the encoder-decoder and the GAN models are made available on Github.1
AB - Deep learning models need a lot of labeled data to work well. In this study, we use a Self-Supervised Learning (SSL) method for semantic segmentation of archaeological monuments in Digital Terrain Models (DTMs). This method first uses unlabeled data to pretrain a model (pretext task), and then fine-tunes it with a small labeled dataset (downstream task). We use unlabeled DTMs and Relief Visualizations (RVs) to train an encoder-decoder and a Generative Adversarial Network (GAN) in the pretext task and an annotated DTM dataset to fine-tune a semantic segmentation model in the downstream task. Experiments indicate that this approach produces better results than training from scratch or using models pretrained on image data like ImageNet. The code and pretrained weights for the encoder-decoder and the GAN models are made available on Github.1
KW - Archaeology
KW - Convolutional Neural Networks
KW - Deep Learning
KW - Digital Terrain Models
KW - Generative Adversarial Networks
KW - Relief Visualization
KW - Self-Supervised Learning
UR - http://www.scopus.com/inward/record.url?scp=85179966569&partnerID=8YFLogxK
U2 - 10.5334/jcaa.110
DO - 10.5334/jcaa.110
M3 - Article
AN - SCOPUS:85179966569
VL - 6
SP - 155
EP - 173
JO - Journal of Computer Applications in Archaeology
JF - Journal of Computer Applications in Archaeology
IS - 1
ER -