Visual Content Analysis and Linked Data for Automatic Enrichment of Architecture-Related Images

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Abstract

Built form dominates the urban space where most people live and work and provides a visual reflection of the local, regional and global esthetical, social, cultural, technological and economic factors and values. Street-view images and historical photo archives are therefore an invaluable source for sociological or historical study; however, they often lack metadata to start any comparative analysis. Date and location are two basic annotations often missing from historical images. Depending on the research question other annotations might be useful, that either could be visually derived (e.g. the number or age of cars, the fashion people wear, the amount of street decay) or extracted from other data sources (e.g. crime statistics for the neighborhood where the picture was taken). Recent advances in automatic visual analysis and the increasing amount of linked open data triggered the research described in this paper. We provide an overview of the current status of automated image analysis and linked data technology and present a case study and methodology to automatically enrich a large database of historical images of buildings in the city of Amsterdam.
Original languageEnglish
Title of host publicationDigital Cultural Heritage
EditorsHorst Kremers
Place of PublicationCham, Switzerland
PublisherSpringer
Pages279-293
Number of pages15
ISBN (Electronic)978-3-030-15200-0
ISBN (Print)978-3-030-15198-0
DOIs
Publication statusPublished - 2019

Keywords

  • Architectural history
  • Architectural heritage
  • Computer vision
  • Linked open data
  • Automated image analysis

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