This article introduces artificial neural networks as a computational tool to utilize legacy archaeological data for precisely and accurately estimating dates of residential site occupation. The implementation of this deep learning algorithm can provide high-resolution demographic reconstructions of a study area from non-collection, non-invasive, and non-destructive data collection methods that only record frequencies of artifact types on the contemporary ground surface. The utility of this deep learning algorithm is presented through an example from the central Mesa Verde region in the northern US Southwest. Results show a properly trained artificial neural network predicts annual residential occupation with an average 92.8% accuracy from AD 450–1300. An annual demographic reconstruction of the central Mesa Verde region using occupation predictions from the artificial neural network is also presented.

本文介绍了人工神经网络作为一种计算工具，利用遗留的考古数据来精确和准确地估计住宅遗址占用的日期。这种深度学习算法的实施可以通过非收集、非侵入性和非破坏性数据收集方法提供研究区域的高分辨率人口重建，这些方法仅记录当代地表上人工制品类型的频率。这种深度学习算法的效用通过美国西南部北部梅萨维德中部地区的一个例子来展示。结果显示，经过适当训练的人工神经网络可以预测公元 450-1300 年间每年的住宅占用率，平均准确率为 92.8%。