Many Maya archaeological areas are not comprehensively or systematically mapped because ruins, often hidden under tropical forest canopy in rugged terrain, can take decades to locate, identify, and map. Recent years have seen an explosion of lidar data collection, and machine learning provides a way to exploit these lidar data, making feature analyses more efficient and consistently executed. At present, there are a limited number of small, area-specific models that exist for the Maya area, the largest of which covers 230 km. Here we present the foundation for a broadscale, multi-area-based convolutional neural network (CNN) object detection model that uses airborne laser scanning data, or lidar, for archaeological feature detection across 615 km of the Maya area, as well as preliminary results from an additional 885 km test area. This sets the path for a model that will enable researchers to map archaeological areas across the entire Maya Lowland area in weeks or months instead of decades. Notably, we find that a model trained on multiple areas with significantly different topographies produces better results for all areas as compared to a model trained on a single area. The broadscale model here presented produced an F1 score of 0.80. Results also include many potential new structure detections, including detections on lidar at an archaeological area that has not yet been comprehensively ground-surveyed and is located in an entirely different country in the Maya Lowlands from where the model was trained on. This model represents an attempt at a broadscale machine learning approach for archaeological feature mapping in the Maya area and demonstrates how big data can be integrated into traditional archaeological workflows. Lidar has already shown much greater ancient Maya infrastructure throughout the Maya world and elsewhere in the tropics, and this study using machine learning with lidar is showing even greater Maya infrastructure through vast areas of the Maya tropical forest.

中文翻译：

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用于玛雅地区考古特征检测的大规模深度学习模型


许多玛雅考古地区没有全面或系统地绘制地图，因为遗址通常隐藏在崎岖地形的热带森林树冠下，可能需要数十年的时间才能定位、识别和绘制地图。近年来，激光雷达数据收集呈爆炸式增长，机器学习提供了一种利用这些激光雷达数据的方法，使特征分析更加高效且一致地执行。目前，玛雅地区存在的小型特定区域模型数量有限，其中最大的覆盖范围为 230 公里。在这里，我们介绍了大规模、基于多区域的卷积神经网络 (CNN) 物体检测模型的基础，该模型使用机载激光扫描数据（或激光雷达）对玛雅地区 615 公里的考古特征进行检测，以及初步结果来自另外 885 公里的测试区域。这为模型奠定了基础，使研究人员能够在几周或几个月而不是几十年内绘制整个玛雅低地地区的考古区域地图。值得注意的是，我们发现，与在单个区域上训练的模型相比，在地形显着不同的多个区域上训练的模型可以为所有区域产生更好的结果。这里展示的大规模模型的 F1 分数为 0.80。结果还包括许多潜在的新结构检测，包括在尚未进行全面地面调查的考古区域对激光雷达的检测，该考古区域位于玛雅低地的另一个国家，与模型训练的地方完全不同。该模型代表了玛雅地区考古特征映射的大规模机器学习方法的尝试，并演示了如何将大数据集成到传统考古工作流程中。 激光雷达已经在整个玛雅世界和热带其他地方展示了更强大的古代玛雅基础设施，而这项利用激光雷达进行机器学习的研究通过玛雅热带森林的大片区域展示了更强大的玛雅基础设施。