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 km2 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.
@article{arxiv.2412.05203,
title = {Archaeoscape: Bringing Aerial Laser Scanning Archaeology to the Deep Learning Era},
author = {Yohann Perron and Vladyslav Sydorov and Adam P. Wijker and Damian Evans and Christophe Pottier and Loic Landrieu},
journal= {arXiv preprint arXiv:2412.05203},
year = {2024}
}