Creating large, high-quality geospatial datasets from historical maps using novice volunteers

Adela Sobotkova*, Shawn A. Ross, Christian Nassif-Haynes, Brian Ballsun-Stanton

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

3 Citations (Scopus)
9 Downloads (Pure)

Abstract

Unlocking data from historical maps for landscape analysis is costly. Automatic extraction using Machine Learning (ML) requires extensive preparation and expertise. Crowdsourcing scales better than direct digitisation by experts, but requires an appropriate platform and the technical skills to adapt it. Existing research provides little guidance as to when investments in these approaches become worthwhile. Here we present a customisation of the Field Acquired Information Management Systems (FAIMS) Mobile platform tailored to offer a streamlined, collaborative system for crowdsourcing map digitisation by volunteers with no prior GIS experience. Deployed in Bulgaria as an ancillary activity during 2017–2018 archaeological fieldwork, FAIMS Mobile was used to digitise 10,827 mound features from Soviet military topographic maps. This digitisation required 241 person-hours (57 from staff; 184 from novice volunteers), with an error rate under 6%. The resulting dataset was consistent, well-documented, and ready for analysis with a few hours of processing. A conservative estimate based on our work suggests our crowdsourcing approach is most efficient for digitisation projects of 10,000–60,000 features, but may offer advantages for datasets as small as a few hundred records. Furthermore, it indicates that systems designed for field data collection, running on mobile devices, can be profitably customised to serve as participatory geospatial data systems accessible to novice volunteers.

Original languageEnglish
Article number102967
JournalApplied Geography
Volume155
ISSN0143-6228
DOIs
Publication statusPublished - Jun 2023

Keywords

  • Bulgaria
  • Crowdsourcing
  • FAIR data
  • Historical maps
  • Landscape archaeology
  • Machine learning
  • Mobile computing
  • Participatory GIS

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