Computer Vision

Britain's Ordnance Survey created the first comprehensive, detailed picture of Great Britain starting in the early nineteenth century, producing tens of thousands of map sheets across multiple series and editions. Thanks to digitization efforts by the National Library of Scotland, anyone can now browse these collections online, but researchers face a fundamental challenge: how do you analyze thousands of maps simultaneously rather than viewing them sheet by sheet? MapReader addresses this through a radical reimagining of maps as computational data. Rather than manually tracing features into Geographic Information Systems with pixel-level precision, MapReader divides map images into user-defined patches, treating each grid square as a unit for creative labeling and automated classification. This epistemological shift rejects the notion that maps are objective records of landscapes, instead embracing them as historical arguments about space and place. The patch-based approach enables computational map studies at previously impossible scales, revealing spatial patterns across local, regional, and national levels while maintaining the critical interpretive lens essential to humanities inquiry. Summary MapReader represents an epistemological shift in how historians and humanities scholars engage with digitized map collections at scale. Developed through the Living with Machines project, this chapter introduces computational map studies as a new field that combines scholarly traditions of map interpretation with computational methods designed for analyzing entire collections rather than individual sheets. ...

Predicting crop yields under changing climate conditions requires understanding how individual plant components develop over time, but manually measuring leaves, flowers, and pods across thousands of high-resolution images is impractical. This study adapts MapReader, originally developed for analyzing historical maps, to automatically segment and classify plant structures in whole oilseed rape (Brassica napus) images. Panel A shows the original plant image, while panels B-D compare three modeling approaches: 6-label multi-class classification (B), chain of binary classifiers (C), and the top-performing combined approach (D) that first separates plant from background, then classifies five plant structures (branches in light yellow, pods in orange, leaves in red, buds in magenta, flowers in purple). The combined approach achieved macro-averaged F1-score of 88.50 and weighted F1-score of 97.71, matching MapReader's performance on historical maps. This interdisciplinary transfer demonstrates how computer vision methods can cross domains from digital humanities to agricultural science, enabling automated phenotyping that could help ensure future food security by integrating genetic and environmental factors into crop yield models. Abstract Introduction: Recent advancements in sensor technologies have enabled collection of many large, high-resolution plant images datasets that could be used to non-destructively explore the relationships between genetics, environment and management factors on phenotype or the physical traits exhibited by plants. The phenotype data captured in these datasets could then be integrated into models of plant development and crop yield to more accurately predict how plants may grow as a result of changing management practices and climate conditions, better ensuring future food security. However, automated methods capable of reliably and efficiently extracting meaningful measurements of individual plant components (e.g. leaves, flowers, pods) from imagery of whole plants are currently lacking. In this study, we explore interdisciplinary application of MapReader, a computer vision pipeline for annotating and classifying patches of larger images that was originally developed for semantic exploration of historical maps, to time-series images of whole oilseed rape (Brassica napus) plants. ...

MapReader is an open-source software library that transforms how researchers extract information from large image collections, particularly historical maps. This diagram illustrates the modular pipeline architecture and data flow through two core tasks: patch classification (dividing images into small cells and classifying visual features) and text spotting (detecting and recognizing text). Starting from input images (top), users can download maps, annotate patches manually, train computer vision models, and perform inference at scale. The flexible pipeline accommodates both small manually-annotated datasets and large-scale automated analysis, as demonstrated by processing approximately 30.5 million patches in one study. Inspired by biomedical imaging methods and adapted for historians, MapReader has proven its versatility by successfully transferring to plant phenotype research, showcasing the power of open and reproducible research methods. This release, developed through the Living with Machines project, includes extensive documentation and tutorials designed to make large-scale visual map analysis accessible to historians and researchers across disciplines. Summary MapReader is an interdisciplinary software library for processing digitized maps 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 one study, we annotated 62,020 patches, trained a suite of computer vision models and performed model inference on approximately 30.5 million patches. ...

Historical maps contain rich information about past landscapes, but extracting data from thousands of maps has traditionally required painstaking manual annotation. MapReader automates this process using computer vision, making large-scale map analysis accessible to users without deep learning expertise. The pipeline divides maps into patches (see insets), trains neural networks to recognize visual features like railways (a, shown in red in c,d) and buildings (b, shown in black in c,d), then reconstructs predictions across entire map sheets. Applied to approximately 16,000 nineteenth-century British Ordnance Survey maps (roughly 30.5 million patches), MapReader transforms visual cartographic information into structured, machine-readable data. The resulting datasets can be queried spatially, analyzed for patterns, and linked to other historical sources, enabling researchers to ask questions at scales previously impossible. 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. ...

Although the Ordnance Survey has been the subject of historical research, scholars have not systematically used its maps as primary sources, partly due to technical barriers in accessing and processing large collections. This paper outlines a computer vision pipeline for analyzing thousands of digitized 25-inch Ordnance Survey maps simultaneously rather than individually. The visualization shows digitization coverage across different map editions, revealing where sheet holdings remain undigitized and how coverage varies between editions. By creating item-level metadata and applying machine learning methods to extract spatial features, the 'patchwork method' transforms map collections into interrogable corpora. This approach enables new forms of historical inquiry based on spatial analysis and allows scholars to adopt an overall view of territory. The paper highlights parallels between today's users of digitized maps and nineteenth-century predecessors who faced a similar inflection point as the project to map the nation approached completion. Abstract 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. ...