Skip to main content

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

  • Chapter
  • First Online:
Digital Cultural Heritage

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    The authors are aware of the selective and sectional character of photographic images as well as the possibility of manipulative use of photographs and their falsification. However, at this point the important thing is photography’s ability to capture a situation closely to reality.

  2. 2.

    Nicéphore Niépce, View from the Window at Le Gras, heliographic image, 1826 or 1827.

  3. 3.

    E.g. the TU Delft repository Colonial Architecture and Town Planning. URL: http://colonialarchitecture.eu [access: 12 April 2018].

References

  1. Löffler, Beate; Carola Hein; Tino Mager. „Searching for Meiji-Tokyo: Heterogeneous Visual Media and the Turn to Global Urban History, Digitalization, and Deep Learning“, in: Global Urban History, 20 March 2018. URL: https://globalurbanhistory.com/2018/03/20/searching-for-meiji-tokyo-heterogeneous-visual-media-and-the-turn-to-global-urban-history-digitalization-and-deep-learning/ [access: 12 April 2018].

  2. Berners-Lee, Tim, James Hendler, and Ora Lassila. “The semantic web.” Scientific American 284.5 (2001): 34–43.

    Article  Google Scholar 

  3. Bauer, Florian, and Martin Kaltenböck. “Linked open data: The essentials.” Edition mono/monochrom, Vienna (2011).

    Google Scholar 

  4. Wick M, Vatant B, Christophe B. Geonames ontology. URL http://www.geonames.org/ontology. 2015. Here: http://www.geonames.org/2759899/about.rdf [access: 20 March 2018].

  5. The Getty Research Institute: Art & Architecture Thesaurus® Online. URL: http://www.getty.edu/research/tools/vocabularies/aat/index.html Here: http://vocab.getty.edu/page/aat/300140808 [access: 20 March 2018].

  6. https://www.w3.org/RDF/.

  7. Isaac, Antoine, and Bernhard Haslhofer. “Europeana linked open data–data. europeana. eu.” Semantic Web 4.3 (2013): 291–297.

    Google Scholar 

  8. Ronzino, Paola, Achille Felicetti, and Sara Di Giorgio. “Integrating Archaeological Datasets: the ARIADNE Portal.” AI* CH@ AI* IA. 2016.

    Google Scholar 

  9. Meroño-Peñuela, Albert, Ashkan Ashkpour, Marieke Van Erp, Kees Mandemakers, Leen Breure, Andrea Scharnhorst, Stefan Schlobach, and Frank Van Harmelen. “Semantic technologies for historical research: A survey.” Semantic Web 6, no. 6 (2015): 539–564.

    Article  Google Scholar 

  10. Van Aggelen, Astrid; Hollink, Laura; Kemman, Max; Kleppe, Martijn; Beunders, Henri, The debates of the European Parliament as Linked Open Data, Semantic Web, vol. Preprint, no. Preprint, pp. 1–10, 2016. https://doi.org/10.3233/sw-160227.

    Article  Google Scholar 

  11. Shadbolt, Nigel, Kieron O’Hara, Tim Berners-Lee, Nicholas Gibbins, Hugh Glaser, and Wendy Hall. “Linked open government data: Lessons from data. gov. uk.” IEEE Intelligent Systems 27, no. 3 (2012): 16–24.

    Article  Google Scholar 

  12. Schreiber, Guus, et al. “Semantic annotation and search of cultural-heritage collections: The MultimediaN ECulture demonstrator.” Web Semantics: Science, Services and Agents on the World Wide Web 6.4 (2008): 243–249.

    Article  Google Scholar 

  13. Victor de Boer, Jur Leinenga, Matthias van Rossum and Rik Hoekstra. Dutch Ships and Sailors Linked Data Cloud. In Proceedings of the International Semantic Web Conference (ISWC 2014), 19–23 October, Riva del Garda, Italy, 2014 (p. 229–244).

    Google Scholar 

  14. Niels Ockeloen, Antske Fokkens, Serge ter Braake, Piek Vossen, Victor de Boer, Guus Schreiber, and Susan Legene. BiographyNet: Managing Provenance at multiple levels and from different perspectives. 3rd International Workshop on Linked Science 2013—Supporting Reproducibility, Scientific Investigations and Experiments.

    Google Scholar 

  15. http://www.clariah.nl [access: 15 March 2018].

  16. Relja Arandjelovic, Petr Gronat, Akihiko Torii, Tomas Pajdla, and Josef Sivic. NetVLAD: CNN architecture for weakly supervised place recog- nition. CoRR, abs/1511.07247, 2015.

    Google Scholar 

  17. Albert Gordo, Jon Almazan, Jerome Revaud, and Diane Larlus. Deep image retrieval: Learning global representations for image search. CoRR, abs/1604.01325, 2016.

    Google Scholar 

  18. Matthew D. Zeiler and Rob Fergus. Visualizing and understanding convolutional networks. CoRR, abs/1311.2901, 2013.

    Google Scholar 

  19. Jason Yosinski, Jeff Clune, Anh Mai Nguyen, Thomas J. Fuchs, and Hod Lipson. Understanding neural networks through deep visualization. CoRR, abs/1506.06579, 2015.

    Google Scholar 

  20. The Common Lab Research Infrastructure for the Arts and Humanities (CLARIAH), URL: https://www.clariah.nl/en/.

  21. Wolfgang Coy and Gertraud Koch in a public discussion at Embedded Digitalities in Basel, 5 April 2018.

    Google Scholar 

  22. Harari, Yuval Noah. Homo Deus – A Brief History of Tomorrow. London: Vintage, 2017. pp. 136, 137–40, 361–2, 462.

    Google Scholar 

  23. The beeldbank archive of historical images from Amsterdam, The Netherlands. https://beeldbank.amsterdam.nl/.

  24. https://beeldbank.amsterdam.nl/beeldbank/weergave/record/?id=B00000013972.

  25. https://data.adamlink.nl/saa/Beeldbank/table?subject=http%3A%2F%2Fbeeldbank.amsterdam.nl%2Fafbeelding%2FB00000013972.

  26. https://www.google.com/maps/search/De+zijgevel+van+Brouwersgracht+137+en+links+Brouwersgracht+208-212/@52.3815462,4.88439,17z/data=!3m1!4b1.

  27. https://www.mapillary.com/developer/api-documentation/.

  28. https://d1cuyjsrcm0gby.cloudfront.net/yyHfvTjH7HEd_kRbBRxr2A/thumb-2048.jpg.

  29. Mei Wang, Weihong Deng.”Deep Visual Domain Adaptation: A Survey”, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China., arXiv:1802.03601v3 [cs.CV] 24 Apr 2018.

  30. Carl Doersch, Saurabh Singh, Abhinav Gupta, Josef Sivic, and Alexei A. Efros. “What makes Paris look like Paris?” ACM Trans. Graph., 31(4):101:1–101:9, July 2012.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tino Mager .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Mager, T., Khademi, S., Siebes, R., Hein, C., Boer, V.d., Gemert, J.v. (2020). Visual Content Analysis and Linked Data for Automatic Enrichment of Architecture-Related Images. In: Kremers, H. (eds) Digital Cultural Heritage. Springer, Cham. https://doi.org/10.1007/978-3-030-15200-0_18

Download citation

Publish with us

Policies and ethics