Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MapReader: A Computer Vision Pipeline for the Semantic Exploration of
  Maps at Scale   We present MapReader, a free, open-source software library written in Python
for analyzing large map collections (scanned or born-digital). This library
transforms the way historians can use maps by turning extensive, homogeneous
map sets into searchable primary sources. MapReader allows users with little or
no computer vision expertise to i) retrieve maps via web-servers; ii)
preprocess and divide them into patches; iii) annotate patches; iv) train,
fine-tune, and evaluate deep neural network models; and v) create structured
data about map content. We demonstrate how MapReader enables historians to
interpret a collection of $\approx$16K nineteenth-century Ordnance Survey map
sheets ($\approx$30.5M patches), foregrounding the challenge of translating
visual markers into machine-readable data. We present a case study focusing on
British rail infrastructure and buildings as depicted on these maps. We also
show how the outputs from the MapReader pipeline can be linked to other,
external datasets, which we use to evaluate as well as enrich and interpret the
results. We release $\approx$62K manually annotated patches used here for
training and evaluating the models.

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The categories of this paper are: stat
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The categories of this paper are: bayes-an
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The categories of this paper are: funct-an
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The categories of this paper are: acc-phys
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The categories of this paper are: comp-gas
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The categories of this paper are: dg-ga
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The categories of this paper are: math-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-ph
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The categories of this paper are: eess
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The categories of this paper are: hep-th
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The categories of this paper are: adap-org
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The categories of this paper are: hep-lat
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The categories of this paper are: q-fin
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The categories of this paper are: chem-ph