@inproceedings{985246b18820438fbe68e05455ef8cd2,
title = "Deep Learning from History: Unlocking Historical Visual Sources Through Artificial Intelligence",
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.",
keywords = "Architectural history, Computer vision, Crowdsourcing, Mixing methods",
author = "Seyran Khademi and Tino Mager and Ronald Siebes",
year = "2021",
doi = "10.1007/978-3-030-93186-5_10",
language = "English",
isbn = "978-3-030-93185-8",
series = "Communications in Computer and Information Science",
publisher = "Springer",
pages = "213--233",
editor = "Florian Niebling and Sander M{\"u}nster and Heike Messemer",
booktitle = "Research and Education in Urban History in the Age of Digital Libraries",
note = "2nd International Conference on Research and Education in Urban History in the Age of Digital Libraries, UHDL 2019 ; Conference date: 10-10-2019 Through 11-10-2019",
}