Deep Learning-Based Road Extraction From Historical Maps

Cengiz Avci, Elif Sertel*, Mustafa Erdem Kabadayi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Automatic road extraction from historical maps is an important task to understand past transportation conditions and conduct spatiotemporal analysis revealing information about historical events and human activities over the years. This research aimed to propose the ideal architecture, encoder, and hyperparameter settings for the historical road extraction task. We used a dataset including 7076 patches with the size of 256 ×256 pixels generated from scanned historical Deutsche Heereskarte 1:200 000 Türkei (DHK 200 Turkey) maps and their corresponding digitized ground truth masks for five different roads types. We first tested the widely used Unet++ and Deeplabv3 architectures. We also evaluated the contribution of attention models by implementing Unet++ with the concurrent spatial and channel-squeeze and excitation block and multiscale attention net. We achieved the best results with split-attention network (Timm-resnest200e) encoder and Unet++ architecture, with 98.99% overall accuracy, 41.99% intersection of union, 51.41% precision, 69.7% recall, and 57.72% F1 score values. Our output weights could be directly used for the inference of other DHK maps and transfer learning for similar or different historical maps. The proposed architecture could also be implemented in different road extraction studies.

Original languageEnglish
Article number3513605
JournalIEEE Geoscience and Remote Sensing Letters
Volume19
DOIs
Publication statusPublished - 2022

Bibliographical note

Publisher Copyright:
© 2004-2012 IEEE.

Funding

This work was supported by the European Research Council (ERC) Project “Industrialisation and Urban Growth from the mid-nineteenth century Ottoman Empire to Contemporary Turkey in a Comparative Perspective, 1850-2000” under the European Union’s Horizon 2020 Research and Innovation Program, acronym UrbanOccupationsOETR, under Grant 679097.

FundersFunder number
Horizon 2020 Framework Programme679097
European Research Council

    Keywords

    • Convolutional neural networks
    • historical maps
    • multiclass road segmentation
    • road type detection

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