Deep Learning from History

Unlocking Historical Visual Sources Through Artificial Intelligence

Conference paper (2021)

Authors

S. Khademi History, Form & Aesthetics - Architecture and the Built Environment
Tino Mager History, Form & Aesthetics - Architecture and the Built Environment
R.M. Siebes History, Form & Aesthetics - Architecture and the Built Environment
Research Group
History, Form & Aesthetics (Architecture and the Built Environment) (TU Delft)
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Published Date

2021

Language

English

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Faculty

Architecture and the Built Environment

Department

Architecture

Research Group

History, Form & Aesthetics

Abstract

Historical photos of towns and villages contain a great deal of information about the built environment of the past. However, it is difficult to evaluate the information of images that are not labeled or incorrectly labeled or not organized in repositories or collections. In order to make the sheer volume of images that are not tagged with metadata found on the Internet or in institutional archives accessible for research, an automated recognition of the image content, in this case of buildings, is necessary. Computer vision can help to address this problem and enable the identification of historical image content. This article describes how artificial intelligence and crowdsourcing are used to identify buildings in nearly half a million historical images of the city of Amsterdam. It explains how computer science and humanities disciplines are linked together to accomplish this task.

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