MapReader: a computer vision pipeline for the semantic exploration of maps at scale
Name
3557919.3565812.pdf
Description
visibility:open
Size
2.31 MB
Format
Adobe PDF
Checksum (CRC64NVME)
14DJscLCFi8=
Resource type
Conference paper (published)
Date published
November 11, 2022
Abstract
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.
Contributor (organisation)
British Library
Living with Machines
Project(s)
Living with Machines
Funder
| Funder name | Awards |
AHRC | AH/S01179X/1 |
Engineering and Physical Sciences Research Council | EP/N510129/1 |
Event title
SIGSPATIAL '22: The 30th International Conference on Advances in Geographic Information
Publisher
Association for Computing Machinery (ACM)
Official URL
Keywords
Collection