Katherine McDonough

We release the historical geographic layer of individual building footprints for late Victorian London. This dataset results from the high-quality automatic extraction of the Ordnance Survey’s (OS) five-feet to the mile (1:1,056) map of London. The original map was surveyed between 1891 and 1895 and published between 1893 and 1896. The extracted vector layer comprises over 1.3 million building footprints, covering an area of ∼450 sq km (≈170 sq miles), detected with a precision of 97% and a recall of 95%.

MapReader is an interdisciplinary software library for processing digitized maps (Hosseini et al., 2022a) and other types of images with two tasks: patch classification and text spotting. Patch classification works by 'patching' images into small, custom-sized cells which are then classified according to the user's needs. Text spotting detects and recognizes text. MapReader offers a flexible pipeline which can be used both for manual annotation of small datasets as well as for computer-vision-based inference of large collections. As an example, in Hosseini et al. (2022a), we annotated 62,020 patches, trained a suite of computer vision models and performed model inference on approximately 30.5 million patches.

Although the Ordnance Survey has itself been the subject of historical research, scholars have not systematically used its maps as primary sources of information. This is partly for disciplinary reasons and partly for the technical reason that high-quality maps have not until recently been available digitally, geo-referenced, and in color. A final, and crucial, addition has been the creation of item-level metadata which allows map collections to become corpora which can for the first time be interrogated en masse as source material. By applying new Computer Vision methods leveraging machine learning, we outline a research pipeline for working with thousands (rather than a handful) of maps at once, which enables new forms of historical inquiry based on spatial analysis. Our ‘patchwork method’ draws on the longstanding desire to adopt an overall or ‘complete’ view of a territory, and in so doing highlights certain parallels between the situation faced by today’s users of digitized maps, and a similar inflexion point faced by their predecessors in the nineteenth century, as the project to map the nation approached a form of completion.

In this half-day tutorial, we will review the basic concepts of, methods for, and applications of geographic information retrieval, also showing some possible applications in fields such as the digital humanities. The tutorial is organized in four parts. First we introduce some basic ideas about geography, and demonstrate why text is a powerful way of exploring relevant questions. We then introduce a basic end-to-end pipeline discussing geographic information in documents, spatial and multi-dimensional indexing [ 19 ], and spatial retrieval and spatial filtering. After showing a range of possible applications, we conclude with suggestions for future work in the area.

Mariona Coll Ardanuy, Federico Nanni, Kaspar Beelen, Kasra Hosseini, Ruth Ahnert, Jon Lawrence, Katherine McDonough, Giorgia Tolfo, Daniel CS Wilson, Barbara McGillivray. Proceedings of the 28th International Conference on Computational Linguistics. 2020.