Deep Learning from History: Unlocking Historical Visual Sources Through Artificial Intelligence

Seyran Khademi*, Tino Mager, Ronald Siebes

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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.

Original languageEnglish
Title of host publicationResearch and Education in Urban History in the Age of Digital Libraries
Subtitle of host publication2nd International Workshop, UHDL 2019, Revised Selected Papers
EditorsFlorian Niebling, Sander Münster, Heike Messemer
PublisherSpringer
Pages213-233
Number of pages21
ISBN (Electronic)978-3-030-93186-5
ISBN (Print)978-3-030-93185-8
DOIs
Publication statusPublished - 2021
Event2nd International Conference on Research and Education in Urban History in the Age of Digital Libraries, UHDL 2019 - Dresden, Germany
Duration: 10 Oct 201911 Oct 2019

Publication series

NameCommunications in Computer and Information Science
Volume1501 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference2nd International Conference on Research and Education in Urban History in the Age of Digital Libraries, UHDL 2019
Country/TerritoryGermany
CityDresden
Period10/10/1911/10/19

Keywords

  • Architectural history
  • Computer vision
  • Crowdsourcing
  • Mixing methods

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