Carleton W Christopher, Klassen Sarah, Niles-Weed Jonathan, Evans Damian, Roberts Patrick, Groucutt Huw S
Extreme Events Research Group, Max Planck Institutes of/for, Geoanthropology, Chemcial Ecology, and Biogeochemistry, Jena, Germany.
Department of Anthropology, University of Toronto, Toronto, Canada.
Sci Rep. 2023 Oct 20;13(1):17913. doi: 10.1038/s41598-023-44875-0.
Lidar (light-detection and ranging) has revolutionized archaeology. We are now able to produce high-resolution maps of archaeological surface features over vast areas, allowing us to see ancient land-use and anthropogenic landscape modification at previously un-imagined scales. In the tropics, this has enabled documentation of previously archaeologically unrecorded cities in various tropical regions, igniting scientific and popular interest in ancient tropical urbanism. An emerging challenge, however, is to add temporal depth to this torrent of new spatial data because traditional archaeological investigations are time consuming and inherently destructive. So far, we are aware of only one attempt to apply statistics and machine learning to remotely-sensed data in order to add time-depth to spatial data. Using temples at the well-known massive urban complex of Angkor in Cambodia as a case study, a predictive model was developed combining standard regression with novel machine learning methods to estimate temple foundation dates for undated Angkorian temples identified with remote sensing, including lidar. The model's predictions were used to produce an historical population curve for Angkor and study urban expansion at this important ancient tropical urban centre. The approach, however, has certain limitations. Importantly, its handling of uncertainties leaves room for improvement, and like many machine learning approaches it is opaque regarding which predictor variables are most relevant. Here we describe a new study in which we investigated an alternative Bayesian regression approach applied to the same case study. We compare the two models in terms of their inner workings, results, and interpretive utility. We also use an updated database of Angkorian temples as the training dataset, allowing us to produce the most current estimate for temple foundations and historic spatiotemporal urban growth patterns at Angkor. Our results demonstrate that, in principle, predictive statistical and machine learning methods could be used to rapidly add chronological information to large lidar datasets and a Bayesian paradigm makes it possible to incorporate important uncertainties-especially chronological-into modelled temporal estimates.
激光雷达（光探测与测距）给考古学带来了变革。如今，我们能够绘制大面积考古地表特征的高分辨率地图，从而在前所未有的尺度上了解古代土地利用情况和人为景观改造。在热带地区，这使得记录各个热带区域以前未被考古记录的城市成为可能，引发了科学界和大众对古代热带城市主义的兴趣。然而，一个新出现的挑战是为这大量的新空间数据增添时间深度，因为传统考古调查既耗时又具有内在破坏性。到目前为止，我们只知道有一次尝试将统计和机器学习应用于遥感数据，以便为空间数据增添时间深度。以柬埔寨著名的吴哥大型城市综合体中的寺庙为案例研究，开发了一种预测模型，将标准回归与新颖的机器学习方法相结合，以估计通过遥感（包括激光雷达）识别出的未注明日期的吴哥时期寺庙的建造日期。该模型的预测结果被用于生成吴哥的历史人口曲线，并研究这个重要的古代热带城市中心的城市扩张情况。然而，这种方法有一定的局限性。重要的是，它对不确定性的处理还有改进空间，而且和许多机器学习方法一样，对于哪些预测变量最相关并不透明。在此，我们描述一项新研究，在该研究中我们调查了应用于同一案例研究的另一种贝叶斯回归方法。我们从内部运作、结果和解释效用方面对这两种模型进行比较。我们还使用更新后的吴哥时期寺庙数据库作为训练数据集，从而能够对吴哥寺庙的建造时间以及历史时空城市增长模式做出最新估计。我们的结果表明，原则上，预测性统计和机器学习方法可用于快速为大型激光雷达数据集添加年代信息，并且贝叶斯范式使得将重要的不确定性——尤其是年代方面的不确定性——纳入建模的时间估计成为可能。
