Crowdsourcing as an Analytical Method: Metrology of Smartphone Measurements in Heritage Science

Abstract

This research assesses the precision, repeatability, and accuracy of crowdsourced scientific measurements, and whether their quality is sufficient to provide usable results. Measurements of colour and area were chosen because of the possibility of producing them with smartphone cameras. The quality of the measurements was estimated experimentally by comparing data contributed by anonymous participants in heritage sites with reference measurements of known accuracy and precision. Participants performed the measurements by taking photographs with their smartphones, from which colour and dimensional data could be extracted. The results indicate that smartphone measurements provided by citizen scientists can be used to measure changes in colour, but that the performance is strongly dependent on the measured colour coordinate. The same method can be used to measure areas when the difference in colour with the neighbouring areas is large enough. These results render the method useful in some heritage science contexts, but higher precision would be desirable.

The role of citizen science mobile apps in facilitating a contemporary digital agora

“…Crowd-sensed and crowd-sourced data can also be used for mapping urban environments by creating accurate surface maps (Fan et al 2019), 3D city models (Bshouty et al 2019), or by exploring heat patterns (Yin et al 2021). Such information can help decision-makers to find sustainable solutions for land administration (Du et al 2021;Olteanu-Raimond et al 2018), urban green space planning and management (Heikinheimo et al 2020;Schrammeijer et al 2022), human mobility improvement (Hologa and Riach 2020; Howe 2021), or heritage building management (Brigham et al 2018;Lamas et al 2021).…”

“…The great drawback of VGI derives from the fact that this is considered qualitative information. Therefore, it requires methods for data filtering and data accuracy testing, in some cases AI models (Annis and Nardi, 2019;Brigham et al 2018;Castro-Gutiérrez et al 2022;Omodior et al 2021;Pepe et al 2021). It also needs methods for testing the accuracy of mobile devices (Blair et al 2018;Cabrera et al 2021).…”