{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mapreader-a-computer-vision-pipeline-for-the","title":"MapReader: A Computer Vision Pipeline for the Semantic Exploration of Maps at Scale","arxiv_id":"2111.15592","date":"2021-11-30","proceeding":null,"authors":["Kasra Hosseini","Daniel C. S. Wilson","Kaspar Beelen","Katherine McDonough"],"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.","url_abs":"https://arxiv.org/abs/2111.15592v1","url_pdf":"https://arxiv.org/pdf/2111.15592v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"mapreader-a-computer-vision-pipeline-for-the","repo_url":"https://github.com/living-with-machines/mapreader","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"16k","task_name":"16k"},{"task_slug":"image-classification","task_name":"Image Classification"}],"methods":[],"datasets_introduced":[{"slug":"mapreader-data","name":"MapReader Data","full_name":"in GeoHumanities workshop, SIGSPATIAL 2022"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}