Automatic Road Extraction from Historical Maps Using Deep Learning Techniques: A Regional Case Study of Turkey in a German World War II Map


Ekim B., Sertel E., Kabadayi M. E.

ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, vol.10, no.8, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 10 Issue: 8
  • Publication Date: 2021
  • Doi Number: 10.3390/ijgi10080492
  • Journal Name: ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED)
  • Keywords: convolutional neural networks, road classification, segmentation, deep learning, fully convolutional networks, historical maps, CLASSIFICATION
  • Istanbul Technical University Affiliated: Yes

Abstract

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 Turkei (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_32x4d 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.