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This study introduces a semi-supervised learning method for analyzing historical maps, reducing manual annotation needs. The framework enables effective feature detection on digitized maps across different years using style transfer and AI.

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Area of Science:

  • Geographical Information Science
  • Computer Vision
  • Artificial Intelligence

Background:

  • Digitalization of historical maps and AI advancements offer new research avenues.
  • Supervised learning for historical map analysis is hindered by extensive manual annotation requirements.

Purpose of the Study:

  • To develop a semi-supervised learning method for historical map analysis.
  • To enable cross-year map comparison and anthropogenic studies by transferring annotations.
  • To reduce the labor of manual map annotations.

Main Methods:

  • A novel two-stage framework involving style transfer of topographic maps across years and versions.
  • Application of supervised learning on synthesized maps with transferred annotations.
  • Investigation using four deep neural networks (DNNs): U-Net, FCN, DeepLabV3, and MobileNetV3.

Main Results:

  • The U-Net model achieved high performance metrics ([Formula: see text] and [Formula: see text]) on style-transfer synthesized maps for detecting features like bridges.
  • The proposed framework successfully detected target features on historical maps without manual annotations.
  • U-Net trained on Contrastive Unpaired Translation (CUT) generated data outperformed other configurations by 57.3%.

Conclusions:

  • The proposed semi-supervised learning framework effectively enables analysis of historical maps across different time periods.
  • The method significantly reduces the need for manual annotations, making historical map analysis more efficient.
  • Future research should address remaining challenges and explore further applications of this technique.