Archaeoscape: Bringing Aerial Laser Scanning Archaeology to the Deep Learning Era
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915060374306816 |
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| author | Perron, Yohann Sydorov, Vladyslav Wijker, Adam P. Evans, Damian Pottier, Christophe Landrieu, Loic |
| author_facet | Perron, Yohann Sydorov, Vladyslav Wijker, Adam P. Evans, Damian Pottier, Christophe Landrieu, Loic |
| contents | Airborne Laser Scanning (ALS) technology has transformed modern archaeology by unveiling hidden landscapes beneath dense vegetation. However, the lack of expert-annotated, open-access resources has hindered the analysis of ALS data using advanced deep learning techniques. We address this limitation with Archaeoscape (available at https://archaeoscape.ai/data/2024/), a novel large-scale archaeological ALS dataset spanning 888 km$^2$ in Cambodia with 31,141 annotated archaeological features from the Angkorian period. Archaeoscape is over four times larger than comparable datasets, and the first ALS archaeology resource with open-access data, annotations, and models.
We benchmark several recent segmentation models to demonstrate the benefits of modern vision techniques for this problem and highlight the unique challenges of discovering subtle human-made structures under dense jungle canopies. By making Archaeoscape available in open access, we hope to bridge the gap between traditional archaeology and modern computer vision methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_05203 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Archaeoscape: Bringing Aerial Laser Scanning Archaeology to the Deep Learning Era Perron, Yohann Sydorov, Vladyslav Wijker, Adam P. Evans, Damian Pottier, Christophe Landrieu, Loic Computer Vision and Pattern Recognition Artificial Intelligence Airborne Laser Scanning (ALS) technology has transformed modern archaeology by unveiling hidden landscapes beneath dense vegetation. However, the lack of expert-annotated, open-access resources has hindered the analysis of ALS data using advanced deep learning techniques. We address this limitation with Archaeoscape (available at https://archaeoscape.ai/data/2024/), a novel large-scale archaeological ALS dataset spanning 888 km$^2$ in Cambodia with 31,141 annotated archaeological features from the Angkorian period. Archaeoscape is over four times larger than comparable datasets, and the first ALS archaeology resource with open-access data, annotations, and models. We benchmark several recent segmentation models to demonstrate the benefits of modern vision techniques for this problem and highlight the unique challenges of discovering subtle human-made structures under dense jungle canopies. By making Archaeoscape available in open access, we hope to bridge the gap between traditional archaeology and modern computer vision methods. |
| title | Archaeoscape: Bringing Aerial Laser Scanning Archaeology to the Deep Learning Era |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2412.05203 |