TY - JOUR
T1 - Creating large, high-quality geospatial datasets from historical maps using novice volunteers
AU - Sobotkova, Adela
AU - Ross, Shawn A.
AU - Nassif-Haynes, Christian
AU - Ballsun-Stanton, Brian
N1 - Publisher Copyright:
© 2023 The Authors
PY - 2023/6
Y1 - 2023/6
N2 - 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.
AB - 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.
KW - Bulgaria
KW - Crowdsourcing
KW - FAIR data
KW - Historical maps
KW - Landscape archaeology
KW - Machine learning
KW - Mobile computing
KW - Participatory GIS
UR - http://www.scopus.com/inward/record.url?scp=85152942715&partnerID=8YFLogxK
U2 - 10.1016/j.apgeog.2023.102967
DO - 10.1016/j.apgeog.2023.102967
M3 - Journal article
AN - SCOPUS:85152942715
SN - 0143-6228
VL - 155
JO - Applied Geography
JF - Applied Geography
M1 - 102967
ER -