Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Publication date | 11/11/2022 |
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Host publication | Proceedings of the 6th ACM SIGSPATIAL International Workshop on Geospatial Humanities, GeoHumanities 2022 |
Editors | Ludovic Moncla, Bruno Martins, Katherine McDonough |
Place of Publication | New York, NY |
Publisher | Association for Computing Machinery (ACM) |
Pages | 8-19 |
Number of pages | 12 |
ISBN (electronic) | 9781450395335 |
<mark>Original language</mark> | English |
Event | SIGSPATIAL '22:: The 30th International Conference on Advances in Geographic Information Systems - Seattle, United States Duration: 1/11/2022 → 1/11/2022 |
Conference | SIGSPATIAL '22: |
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Country/Territory | United States |
City | Seattle |
Period | 1/11/22 → 1/11/22 |
Name | Proceedings of the 6th ACM SIGSPATIAL International Workshop on Geospatial Humanities, GeoHumanities 2022 |
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Conference | SIGSPATIAL '22: |
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Country/Territory | United States |
City | Seattle |
Period | 1/11/22 → 1/11/22 |
We present MapReader, a free, open-source software library written in Python for analyzing large map collections. MapReader allows users with little 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 ≈16K nineteenth-century maps of Britain (≈30.5M patches), foregrounding the challenge of translating visual markers into machine-readable data. We present a case study focusing on rail and buildings. We also show how the outputs from the MapReader pipeline can be linked to other, external datasets. We release ≈62K manually annotated patches used here for training and evaluating the models.