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

1
Satellite Communication and Remote Sensing Program, Institute of Informatics, Istanbul Technical University, Istanbul 34469, Turkey
2
Department of History, College of Social Sciences and Humanities, Koç University, Rumelifeneri Yolu, Istanbul 34450, Turkey
3
Geomatics Engineering Department, Istanbul Technical University, Istanbul 34469, Turkey
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Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2021, 10(8), 492; https://doi.org/10.3390/ijgi10080492
Received: 25 May 2021 / Revised: 13 July 2021 / Accepted: 18 July 2021 / Published: 21 July 2021
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. View Full-Text
Keywords: convolutional neural networks; road classification; segmentation; deep learning; fully convolutional networks; historical maps convolutional neural networks; road classification; segmentation; deep learning; fully convolutional networks; historical maps
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MDPI and ACS Style

Ekim, B.; Sertel, E.; Kabadayı, M.E. Automatic Road Extraction from Historical Maps Using Deep Learning Techniques: A Regional Case Study of Turkey in a German World War II Map. ISPRS Int. J. Geo-Inf. 2021, 10, 492. https://doi.org/10.3390/ijgi10080492

AMA Style

Ekim B, Sertel E, Kabadayı ME. Automatic Road Extraction from Historical Maps Using Deep Learning Techniques: A Regional Case Study of Turkey in a German World War II Map. ISPRS International Journal of Geo-Information. 2021; 10(8):492. https://doi.org/10.3390/ijgi10080492

Chicago/Turabian Style

Ekim, Burak, Elif Sertel, and M. E. Kabadayı 2021. "Automatic Road Extraction from Historical Maps Using Deep Learning Techniques: A Regional Case Study of Turkey in a German World War II Map" ISPRS International Journal of Geo-Information 10, no. 8: 492. https://doi.org/10.3390/ijgi10080492

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