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Map Archive Mining: Visual-Analytical Approaches to Explore Large Historical Map Collections.

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Automating the extraction of geographical information from historical map archives is crucial due to large data volumes and variable quality. Preliminary analysis of map coverage, content, and accuracy is essential for successful computational processing.

Keywords:
Sanborn fire insurance mapsUSGS topographic mapscolor momentsimage information mininglow-level image descriptorsmap processingretrospective landscape analysist-distributed stochastic neighborhood embeddingvisual data mining

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Area of Science:

  • Geographic Information Science
  • Digital Cartography
  • Data Science

Background:

  • Historical map archives offer valuable retrospective geographical data.
  • Digitized map archives enable geospatial analysis beyond the digital cartography era.
  • Large data volumes and low graphical quality of historical maps necessitate automated information extraction.

Purpose of the Study:

  • To address challenges in automating large-scale information extraction from historical map archives.
  • To demonstrate the importance of preliminary analytical steps for map processing.
  • To assess spatio-temporal coverage, map content, and spatial accuracy of georeferenced map sheets.

Main Methods:

  • Utilized traditional analytical and cartographic techniques.
  • Employed visual-analytical data mining tools from machine learning and data science.
  • Applied methods to United States Geological Survey topographic and Sanborn fire insurance map archives.

Main Results:

  • Preliminary analyses are critical for foundational computational processes in map archives.
  • Systematic assessment of map characteristics and data quality variations is feasible.
  • Demonstrated effective preliminary analysis techniques for large-scale map data.

Conclusions:

  • Automating information extraction from historical maps requires robust preliminary analysis.
  • Integrating traditional and data science methods enhances the processing of map archives.
  • These foundational analyses are vital for subsequent recognition and data extraction tasks.