Comparing the quality of crowdsourced data contributed by expert and non-experts.
There is currently a lack of in-situ environmental data for the calibration and validation of remotely sensed products and for the development and verification of models. Crowdsourcing is increasingly being seen as one potentially powerful way of increasing the supply of in-situ data but there are a number of concerns over the subsequent use of the data, in particular over data quality. This paper examined crowdsourced data from the Geo-Wiki crowdsourcing tool for land cover validation to determine whether there were significant differences in quality between the answers provided by experts and non-experts in the domain of remote sensing and therefore the extent to which crowdsourced data describing human impact and land cover can be used in further scientific research. The results showed that there was little difference between experts and non-experts in identifying human impact although results varied by land cover while experts were better than non-experts in identifying the land cover type. This suggests the need to create training materials with more examples in those areas where difficulties in identification were encountered, and to offer some method for contributors to reflect on the information they contribute, perhaps by feeding back the evaluations of their contributed data or by making additional training materials available. Accuracies were also found to be higher when the volunteers were more consistent in their responses at a given location and when they indicated higher confidence, which suggests that these additional pieces of information could be used in the development of robust measures of quality in the future.
International Institute for Applied Systems Analysis, Ecosystem Services and Management Program, Laxenburg, Austria. see@iiasa.ac.at
Linda
L
See
Alexis
A
Comber
Carl
C
Salk
Steffen
S
Fritz
Marijn
M
van der Velde
Christoph
C
Perger
Christian
C
Schill
Ian
I
McCallum
Florian
F
Kraxner
Michael
M
Obersteiner
D000383:Agriculture; D003247:Conservation of Natural Resources / Q000379:methods*; D063045:Crowdsourcing / Q000379:methods* / Q000592:standards*; D017753:Ecosystem; D006802:Human Activities; D006801:Humans; D020407:Internet; D012044:Regression Analysis; D015203:Reproducibility of Results; D013997:Time Factors