Machine learning-ready remote sensing data for Maya archaeology: masks, ALS data, Sentinel-1, Sentinel-2

The dataset includes multimodal annotated data for remote sensing of Maya archaeology and is suitable for deep learning. The dataset covers the area around Chactún, one of the largest ancient Maya urban centres in the central Yucatán peninsula.It includes five types of data:high-resolution airborne laser scanning (ALS, lidar) data visualisations (sky view factor, positive openness, slope),high-resolution airborne laser scanning derived canopy height model,Sentinel-1 Short Aperture Radar (SAR) satellite data (yearly average Sigma0),Sentine-2 optical satellite data (12 bands + cloud mask, 17 dates), andmanual data annotations.The manual annotations (used as binary masks) represent three different types of ancient Maya structures (class labels: buildings, platforms, and aguadas – artificial reservoirs) within the study area, their exact locations, and boundaries.The dataset is ready for use with convolutional neural networks (CNNs) for object recognition, object localization (detection), and semantic segmentation. The dataset has already been used for the <i>Discover the Mysteries of the Maya</i> computer vision competition.We would like to provide this dataset to help more research teams develop their own computer vision models for investigations of Maya archaeology or improve existing ones.A detailed description of the datasets has been published by Kokalj, Ž., Džeroski, S., Šprajc, I. <i>et al.</i> Machine learning-ready remote sensing data for Maya archaeology. <i>Scientific Data</i> <b>10</b>, 558 (2023). https://doi.org/10.1038/s41597-023-02455-x<br>The authors and institutions they are affiliated with exclude all liability for any reliance on the data.本数据集包含面向玛雅考古遥感任务的多模态标注数据，适用于深度学习模型训练。本数据集覆盖尤卡坦半岛中部规模最大的古玛雅城市中心之一——查克顿（Chactún）周边区域。 本数据集涵盖五类数据：高分辨率机载激光扫描（Airborne Laser Scanning, ALS，即激光雷达lidar）数据可视化成果，包含天空视域因子、正向开阔度、坡度三类指标；由高分辨率机载激光扫描数据衍生的冠层高度模型；哨兵-1（Sentinel-1）合成孔径雷达（Short Aperture Radar, SAR）卫星数据，包含年度平均Sigma0值；哨兵-2（Sentinel-2）光学卫星数据，涵盖12个波段与云掩膜图层，共17个观测时相；以及人工标注数据。 上述人工标注以二值掩码形式呈现，对应研究区内三类古玛雅建筑结构（类别标签：建筑、台基与阿瓜达斯（人工水库）），并精确标注了各结构的位置与边界范围。 本数据集可直接用于卷积神经网络（Convolutional Neural Networks, CNNs）的目标识别、目标定位（检测）与语义分割任务。该数据集曾应用于《揭秘玛雅奥秘》（*Discover the Mysteries of the Maya*）计算机视觉竞赛。 我们特此公开本数据集，以期助力更多研究团队开发适用于玛雅考古研究的计算机视觉模型，或对现有模型进行优化升级。 本数据集的详细技术说明已由Kokalj, Ž.、Džeroski, S.、Šprajc, I.等人发表于《科学数据》（Scientific Data）期刊，论文题为《面向玛雅考古的机器学习适配型遥感数据集》（*Machine learning-ready remote sensing data for Maya archaeology*），刊载于该刊第10卷第558页（2023年），DOI链接：https://doi.org/10.1038/s41597-023-02455-x。 数据集作者及其所属机构对任何依赖本数据集开展研究或应用所造成的损失，不承担任何法律责任。