公开了柬埔寨888平方公里的激光雷达考古数据集，助力深度学习发现丛林中隐藏的古代遗迹。
Archaeoscape: Bringing Aerial Laser Scanning Archaeology to the Deep Learning Era
- 构建888平方公里、含31141个标注遗迹的大型激光雷达考古数据集
- 数据量超同类数据集四倍，首次实现数据、标注与模型全开源
- 适合从事考古挖掘、遥感分析或深度学习应用的研究者使用
机载激光雷达（ALS）技术已彻底改变现代考古学，能够揭示茂密植被下的隐藏地貌。然而，缺乏专家标注且开放获取的数据资源，限制了先进深度学习技术在ALS数据分析中的应用。为此，我们推出了Archaeoscape（https://archaeoscape.ai/data/2024/），一个覆盖柬埔寨888平方公里、包含31,141个吴哥时期考古特征的大规模考古激光雷达数据集。该数据集规模超过现有同类数据集四倍，是首个提供开放获取数据、标注与模型的激光雷达考古资源。我们通过基准测试多种最新分割模型，展示了现代视觉技术在此任务中的优势，并强调在密集丛林冠层下发现细微人工结构的独特挑战。通过开放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。
原文摘要 · Abstract (English)
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.
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