Garcia-Molsosa Arnau, Orengo Hector A, Lawrence Dan, Philip Graham, Hopper Kristen, Petrie Cameron A
Landscape Archaeology Research Group (GIAP) Catalan Institute of Classical Archaeology Pl. Rovellat s/n Tarragona 43003 Spain.
Department of Archaeology Durham University South Road Durham DH1 3LE UK.
Archaeol Prospect. 2021 Apr-Jun;28(2):187-199. doi: 10.1002/arp.1807. Epub 2021 Jan 26.
Historical maps present a unique depiction of past landscapes, providing evidence for a wide range of information such as settlement distribution, past land use, natural resources, transport networks, toponymy and other natural and cultural data within an explicitly spatial context. Maps produced before the expansion of large-scale mechanized agriculture reflect a landscape that is lost today. Of particular interest to us is the great quantity of archaeologically relevant information that these maps recorded, both deliberately and incidentally. Despite the importance of the information they contain, researchers have only recently begun to automatically digitize and extract data from such maps as coherent information, rather than manually examine a raster image. However, these new approaches have focused on specific types of information that cannot be used directly for archaeological or heritage purposes. This paper provides a proof of concept of the application of deep learning techniques to extract archaeological information from historical maps in an automated manner. Early twentieth century colonial map series have been chosen, as they provide enough time depth to avoid many recent large-scale landscape modifications and cover very large areas (comprising several countries). The use of common symbology and conventions enhance the applicability of the method. The results show deep learning to be an efficient tool for the recovery of georeferenced, archaeologically relevant information that is represented as conventional signs, line-drawings and text in historical maps. The method can provide excellent results when an adequate training dataset has been gathered and is therefore at its best when applied to the large map series that can supply such information. The deep learning approaches described here open up the possibility to map sites and features across entire map series much more quickly and coherently than other available methods, opening up the potential to reconstruct archaeological landscapes at continental scales.
历史地图呈现了过去地貌的独特描绘，为广泛的信息提供了证据，如聚落分布、过去的土地利用、自然资源、交通网络、地名以及明确空间背景下的其他自然和文化数据。在大规模机械化农业扩张之前绘制的地图反映了如今已消失的一种地貌。我们特别感兴趣的是这些地图有意或无意记录的大量与考古学相关的信息。尽管它们所包含的信息很重要，但研究人员直到最近才开始将此类地图中的数据自动数字化并提取为连贯的信息，而不是手动检查光栅图像。然而，这些新方法聚焦于无法直接用于考古或遗产目的的特定类型信息。本文提供了一个概念验证，即应用深度学习技术以自动化方式从历史地图中提取考古信息。我们选择了20世纪初的殖民地图系列，因为它们提供了足够的时间深度，以避免许多近期大规模的地貌改变，并且覆盖了非常大的区域（包括几个国家）。使用通用的符号和惯例增强了该方法的适用性。结果表明，深度学习是一种有效的工具，可用于恢复以历史地图中的传统符号、线条图和文字形式呈现的地理参考的、与考古学相关的信息。当收集到足够的训练数据集时，该方法可以提供出色的结果，因此在应用于能够提供此类信息的大型地图系列时效果最佳。这里描述的深度学习方法开启了比其他现有方法更快、更连贯地绘制整个地图系列中的遗址和特征的可能性，为在大陆尺度上重建考古地貌开辟了潜力。
