Department of Geography, Alexandru Ioan Cuza University, 700505 Iași, Romania.
Sensors (Basel). 2020 Feb 21;20(4):1192. doi: 10.3390/s20041192.
Archaeological topography identification from high-resolution DEMs (Digital Elevation Models) is a current method that is used with high success in archaeological prospecting of wide areas. I present a methodology through which burial mounds (tumuli) from LiDAR (Light Detection And Ranging) DEMS can be identified. This methodology uses geomorphometric and statistical methods to identify with high accuracy burial mound candidates. Peaks, defined as local elevation maxima are found as a first step. In the second step, local convexity watershed segments and their seeds are compared with positions of local peaks and the peaks that correspond or have in vicinity local convexity segments seeds are selected. The local convexity segments that correspond to these selected peaks are further fed to a Random Forest algorithm together with shape descriptors and descriptive statistics of geomorphometric variables in order to build a model for the classification. Multiple approaches to tune and select the proper training dataset, settings, and variables were tested. The validation of the model was performed on the full dataset where the training was performed and on an external dataset in order to test the usability of the method for other areas in a similar geomorphological and archaeological setting. The validation was performed against manually mapped, and field checked burial mounds from two neighbor study areas of 100 km each. The results show that by training the Random Forest on a dataset composed of between 75% and 100% of the segments corresponding to burial mounds and ten times more non-burial mounds segments selected using Latin hypercube sampling, 93% of the burial mound segments from the external dataset are identified. There are 42 false positive cases that need to be checked, and there are two burial mound segments missed. The method shows great promise to be used for burial mound detection on wider areas by delineating a certain number of tumuli mounds for model training.
从高分辨率数字高程模型 (DEM) 进行考古地形识别是当前在大面积考古勘探中使用的一种非常成功的方法。我提出了一种通过 LiDAR (光探测与测量) DEM 识别土冢（坟墓）的方法。该方法使用地形和统计方法来准确识别土冢候选物。首先找到定义为局部高程最大值的山峰。在第二步中，将局部凸分水岭段及其种子与局部峰的位置进行比较，选择与局部凸分水岭段对应的峰或附近有局部凸分水岭段种子的峰。将与这些选择的峰对应的局部凸分水岭段进一步输入到随机森林算法中，并结合地形形态变量的形状描述符和描述性统计数据，以便为分类建立模型。测试了多种方法来调整和选择合适的训练数据集、设置和变量。模型的验证是在进行训练的完整数据集和外部数据集上进行的，以测试该方法在类似地形和考古环境的其他地区的可用性。验证是针对从两个相邻的研究区域手动绘制并现场检查的土冢进行的，每个区域的面积为 100 公里。结果表明，通过在由对应于土冢的段组成的数据集上训练随机森林，并且使用拉丁超立方采样选择十倍以上的非土冢段，能够识别外部数据集中 93%的土冢段。有 42 个假阳性的情况需要检查，还有两个土冢段被遗漏。该方法有望通过划定一定数量的土冢进行模型训练，在更大的范围内用于土冢检测。
