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.

中文翻译：

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深度学习分割从历史地图系列中提取考古特征的潜力


历史地图对过去的景观进行了独特的描述，为广泛的信息提供了证据，例如在明确的空间背景下的定居点分布、过去的土地利用、自然资源、交通网络、地名和其他自然和文化数据。大规模机械化农业扩张之前制作的地图反映了今天已经消失的景观。我们特别感兴趣的是这些地图有意或无意记录的大量考古相关信息。尽管它们包含的信息很重要，但研究人员最近才开始自动数字化并从此类地图中提取数据作为连贯信息，而不是手动检查光栅图像。然而，这些新方法侧重于不能直接用于考古或遗产目的的特定类型的信息。本文提供了应用深度学习技术以自动方式从历史地图中提取考古信息的概念证明。选择了二十世纪初的殖民地图系列，因为它们提供了足够的时间深度，以避免许多最近的大规模景观修改，并覆盖非常大的区域（包括几个国家）。通用符号体系和约定的使用增强了该方法的适用性。结果表明，深度学习是恢复地理参考、考古相关信息的有效工具，这些信息在历史地图中以传统符号、线条图和文本的形式表示。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，因此当应用于可以提供此类信息的大型地图系列时，该方法可以达到最佳效果。 这里描述的深度学习方法开辟了比其他可用方法更快、更连贯地绘制整个地图系列中的地点和特征的可能性，从而开辟了重建大陆尺度考古景观的潜力。