Automatic extraction of elevation information from the historical maps using machine learning techniques
dc.contributor.advisor
William, Mackaness
dc.contributor.author
Daiqiao, Wu
dc.date.accessioned
2022-11-08T11:29:11Z
dc.date.available
2022-11-08T11:29:11Z
dc.date.issued
2022-11-01
dc.description.abstract
Historical maps are rich data sources of geographic data. However, without transcription, the map data is not machine-readable, meaning they cannot be used by modern GIS technologies. Currently, we still rely on tedious manual work to make the map machine-readable (such as crowdsourcing [34]). In this project, we present an automatic method to extract elevation data in the benchmarks from the Ordnance Survey 6-inch to one-mile historical map. Template matching and optical character recognition are used to locate and identify the numbers in the benchmark. The result shows that this method can efficiently extract the elevation data with minimum manual correction.
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dc.identifier.uri
https://hdl.handle.net/1842/39450
dc.identifier.uri
http://dx.doi.org/10.7488/era/2700
dc.language.iso
en
en
dc.publisher
The University of Edinburgh
en
dc.subject
Historical map
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dc.subject
machine learning
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dc.title
Automatic extraction of elevation information from the historical maps using machine learning techniques
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dc.type
Thesis or Dissertation
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dc.type.qualificationlevel
Masters
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dc.type.qualificationname
MSc Master of Science
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