Scanned historical maps are available from different sources in various scales and contents. Automatic geographical feature extraction from these historical maps is an essential task to derive valuable spatial information on the characteristics and distribution of transportation infrastructures and settlements and to conduct quantitative and geometrical analysis. In this research, we used the Deutsche Heereskarte 1:200,000 Türkei (DHK 200 Turkey) maps as the base geoinformation source to construct the past transportation networks using the deep learning approach. Five different road types were digitized and labeled to be used as inputs for the proposed deep learning-based segmentation approach. We adapted U-Net++ and ResneXt50_32 × 4d architectures to produce multi-class segmentation masks and perform feature extraction to determine various road types accurately. We achieved remarkable results, with 98.73% overall accuracy, 41.99% intersection of union, and 46.61% F1 score values. The proposed method can be implemented in DHK maps of different countries to automatically extract different road types and used for transfer learning of different historical maps.

中文翻译：

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使用深度学习技术从历史地图中自动提取道路：德国二战地图中土耳其的区域案例研究

扫描的历史地图可从不同来源以各种比例和内容获得。从这些历史地图中自动提取地理特征是获取有关交通基础设施和聚落的特征和分布的有价值的空间信息以及进行定量和几何分析的一项基本任务。在这项研究中，我们使用了Deutsche Heereskarte 1:200,000 Türkei(DHK 200 Turkey) 地图作为基础地理信息源，使用深度学习方法构建过去的交通网络。五种不同的道路类型被数字化和标记，用作所提出的基于深度学习的分割方法的输入。我们采用 U-Net++ 和 ResneXt50_32 × 4d 架构来生成多类分割掩码并执行特征提取以准确确定各种道路类型。我们取得了显着的成果，总体准确率为 98.73%，联合交集率为 41.99%，F1 得分值为 46.61%。所提出的方法可以在不同国家的 DHK 地图中实现，以自动提取不同的道路类型，并用于不同历史地图的迁移学习。