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Topographic maps represent the Earth's surface features using contour lines, which connect points of equal elevation to create a two-dimensional representation of three-dimensional terrain. Creating a topographic map requires a systematic approach.Begin by plotting a scaled grid and marking intersections corresponding to the survey's elevation data points. Assign elevation values at these intersections to build the base map. Next, determine contour levels using a consistent contour interval,...
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Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
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Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...
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Related Experiment Video

Updated: Jul 3, 2025

High-speed Particle Image Velocimetry Near Surfaces
11:59

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Automatic vectorization of historical maps: A benchmark.

Yizi Chen1,2,3, Joseph Chazalon1, Edwin Carlinet1

  • 1EPITA Research Lab. (LRE), Kremlin-Bicêtre, France.

Plos One
|February 15, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an improved pipeline for digitizing historical maps, enhancing shape vectorization accuracy and completeness for detailed urban analysis. The research benchmarks deep learning methods for reliable feature extraction from historical atlases.

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Area of Science:

  • Computer Vision
  • Digital Humanities
  • Geographic Information Systems (GIS)

Background:

  • Historical map digitization is crucial for urban studies, but existing shape vectorization methods face accuracy challenges.
  • Digitizing 19th and early 20th-century Paris atlases requires robust techniques for extracting complex geographic details like buildings and streets.

Purpose of the Study:

  • To comprehensively evaluate and improve supervised pipelines for accurate and complete shape vectorization of historical maps.
  • To identify the most effective methodological choices for historical map digitization and feature extraction.

Main Methods:

  • Developed a supervised pipeline combining deep edge filtering and watershed transform for closed shape extraction.
  • Proposed an improved training protocol and joint optimization for edge detection and shape extraction stages.
  • Compared state-of-the-art deep edge filters (including vision transformers) and evaluated deep learnable watershed against traditional methods.

Main Results:

  • Established a benchmark for historical map vectorization, making data, code, and results publicly available.
  • Demonstrated the effectiveness of a joint optimization approach for enhancing vectorization performance.
  • Identified critical paths for fully automatic extraction of key historical map elements.

Conclusions:

  • The proposed pipeline and optimized methods significantly improve the accuracy and completeness of historical map vectorization.
  • This work provides a valuable resource for researchers in digital humanities and GIS, enabling new historical analyses.
  • The open availability of resources facilitates further research and application in historical cartography and urban morphology studies.