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.

翻译：机载激光扫描（ALS）技术通过揭示茂密植被下隐藏的景观，变革了现代考古学。然而，缺乏专家标注的开放获取资源阻碍了利用先进深度学习技术分析ALS数据。我们通过Archaeoscape（可在https://archaeoscape.ai/data/2024/获取）解决了这一局限，这是一个新颖的大规模考古ALS数据集，覆盖柬埔寨888 km$^2$区域，包含31,141个吴哥时期标注的考古特征。Archaeoscape的规模超过同类数据集四倍以上，并且是首个提供开放获取数据、标注和模型的ALS考古资源。我们对若干近期分割模型进行了基准测试，以展示现代视觉技术在此问题上的优势，并强调了在茂密丛林冠层下发现细微人造结构所面临的独特挑战。通过开放获取Archaeoscape，我们希望弥合传统考古学与现代计算机视觉方法之间的鸿沟。