MapReader: A Computer Vision Pipeline for the Semantic Exploration of Maps at Scale

• URL: http://arxiv.org/abs/2111.15592v1
• Date: Tue, 30 Nov 2021 17:37:01 GMT
• Title: MapReader: A Computer Vision Pipeline for the Semantic Exploration of Maps at Scale
• Authors: Kasra Hosseini, Daniel C.S. Wilson, Kaspar Beelen, Katherine McDonough
• Abstract summary: We present MapReader, a free, open-source software library written in Python for analyzing large map collections (scanned or born-digital) MapReader allows users with little or no computer vision expertise to retrieve maps via web-servers. We show how the outputs from the MapReader pipeline can be linked to other, external datasets.
• Score: 1.5894241142512051
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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