Summary of Archaeoscape: Bringing Aerial Laser Scanning Archaeology to the Deep Learning Era, by Yohann Perron et al.
Archaeoscape: Bringing Aerial Laser Scanning Archaeology to the Deep Learning Era
by Yohann Perron, Vladyslav Sydorov, Adam P. Wijker, Damian Evans, Christophe Pottier, Loic Landrieu
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers tackle the limitation of using advanced deep learning techniques for analyzing Airborne Laser Scanning (ALS) data in archaeology. They introduce Archaeoscape, a novel large-scale archaeological ALS dataset spanning 888 km² in Cambodia with 31,141 annotated features from the Angkorian period. The dataset is over four times larger than comparable datasets and the first ALS archaeology resource to offer open-access data, annotations, and models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Archaeologists have long used Airborne Laser Scanning (ALS) technology to uncover hidden landscapes beneath dense vegetation. But analyzing these scans requires expert-annotated resources that are hard to come by. That’s why researchers created Archaeoscape, a massive dataset of ALS data from Cambodia with 31,141 labeled archaeological features. It’s like having a superpowerful tool to help us understand our past! |
Keywords
» Artificial intelligence » Deep learning
