###### Abstract
It took in total 1250 h to digitize and label 300,000 km of roads, including more than 64,000 segments from the _Generalkarte_, another military map [9]. For the digitization of DHK 200 Turkey, we spent around 1500 h extracting roads for a total of 85 images out of 138. We used the original projection system of the map, EPSG: 28405--Pulkovo 1942/Gauss--Kruger zones 5-8, and coordinates written on the borders of individual sheets. Figure 1: DHK 200 Turkey, 3 rd special edition coverage of versions 6612, 6606, and 6613 from 1943. With the increased availability of aerial photographs and satellite images, deep learning methods have become an important research topic in remote sensing for the extraction of geographic information, specifically by applying object detection, classification, and segmentation tasks [10; 11; 12]. [PERSON] et al. [13] provided a comprehensive review on the image scene classification task and described the details of autoencoder-based, convolutional neural network (CNN)-based, and generative adversarial network (GAN)-based image scene classification methods. [PERSON] et al. [14] conducted a complete review for the semantic segmentation of remotely sensed images. In particular, they explained the CNN architectures used in semantic segmentation such as U-Net, SegNet, and DeepLab. Moreover, deep learning-based methods have started to be implemented in historical map-related tasks recently [2; 6; 9; 15]. [PERSON] and [PERSON] [2] proposed a conditional generative adversarial network-based architecture to synthesize satellite images from historical maps that combined the texture information from the input data and reproduced a better visual output of a synthesized satellite image. [PERSON] and [PERSON] [15] used Inception-ResNet v2 architecture based faster region-based deep convolutional neural network (RCNN) method pre-trained on the Microsoft COCO dataset to determine the road intersections of single-line and double-line road symbols from the United States Geological Survey (USGS) maps by considering this as an object detection problem. They found better precision and recall values for double-line road symbols compared with single-line road symbols. Their F1 scores were 0.8 and 0.86 for single- and double-line road symbols, respectively. Their outputs were bounding boxes, and in some cases, the detected boxes were bigger than the ground truth boxes, based on analysis of the figures in their article. However, there was no specific information on the intersection of union (IoU) metrics to quantitatively analyze the match of the geometry between the ground truth and the model's output. It is essential to provide precision, recall, and F1 scores as well as IoU metric values to better quantify the performance of a proposed approach and conduct benchmark analysis among different research outcomes. [PERSON] et al. [6] proposed a weakly supervised CNN-based framework for the extraction of buildings and urban areas from the USGS historical topographic maps published between 1893 and 1954. They compared the results of the VGGNet-16, LeNet, and AlexNet architectures, and the best classification accuracy results were obtained with VGGNet-16, whereas the lowest accuracy was obtained with the shallow LeNet architecture. Afterward, they implemented semantic segmentation in VGGNet-16. [PERSON] et al. [3] applied deep learning-based approaches for the recognition of railroads from 1:24,000 scale USGS historical topographic maps. They manually created the one-pixel-wide ground truth data by digitizing railroad centerlines, applying buffers to both sides, and finally making three-pixel-wide railroad representations. They implemented three different fully convolutional networks (FCNs) with VGG16, GoogLeNet, and a residual network (ResNet) with ImageNet pretrained weights. The best IoU value obtained was 23.09% with the FCN-ResNet architecture due to the lack of training data and the limited ability of the applied models to detect small objects.