###### Abstract  
Scinned 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 Heresbarte 1:200,000 Tiurkei_ (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\(\times\)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. convolutional neural networks; road classification; segmentation; deep learning; fully convolutional networks; historical maps +
Footnote †: journal: Accepted: 18 July 2021  
geographic features such as the spatial distribution of settlements and transportation infrastructures. Logistic information, and more specifically roads, are of central importance for long-term multimodal transport network reconstructions and traffic simulations. Historical maps, especially military maps with their special focus on transport infrastructures, are the best sources to extract valuable logistic data for past transport features, due to the fact that these maps are produced to be used in troop movements in possible military conflicts or planned invasions. During World War II, the German General Staff's Department of Wartime Map and Survey Service (_Generalstab des Heeres, Abteilung fur Kriegskarten und Vermessungswesen_) had around 15,000 military and civil personnel. This unit produced in the relatively short period of the war an astonishing number of maps, totaling approximately 1.3 billion maps [7]. The _Deutsche Heereskarte 1:200,000 Turkei_ (DHK 200 Turkey) series was a part of this massive military cartographical effort to be used during WWII. The DHK 200 Turkey was produced by the Main Survey Department (_Haupttermessungsabteilung_) XIV in Vienna in 1942-1943. It exists in six different versions with around 400 sheets, covering almost the whole of Turkey. Several state and university libraries have copies of the DHK 200 Turkey and made them available online. To our knowledge, the McMaster University Library has the largest, albeit incomplete, available online collection ([[http://digitalarchive.mcmaster.ca/islandora/object/macrepo%3A82339](http://digitalarchive.mcmaster.ca/islandora/object/macrepo%3A82339)]([http://digitalarchive.mcmaster.ca/islandora/object/macrepo%3A82339](http://digitalarchive.mcmaster.ca/islandora/object/macrepo%3A82339)), accessed on 19 July 2021). We acquired the complete 3 rd special issue, version 6612 from the Austrian Federal Office of Metrology and Surveying's Cartography Department/Historical Map Archive in Vienna, which has the archives of the Main Survey Department XIV, the producer of the DHK 200 Turkey, due to the institutional continuity (see Figure 1). The 3 rd special issue has a total of 115 sheets. Some of these are divided into two parts (east and west), and there are a total of 138 images. Vectorization of historical maps and aerial photographs has been conducted mainly through the on-screen digitization technique, which is time-consuming and labor-intensive [4; 8; 9]. Manual digitization has still been widely used for reliable labeled data preparation for use in the learning process of artificial intelligence-based approaches.