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Research article - Peer-reviewed, 2016

Local Knowledge and Professional Background Have a Minimal Impact on Volunteer Citizen Science Performance in a Land-Cover Classification Task

Salk, Carl; Sturn, Tobias; See, Linda; Fritz, Steffen

Abstract

The idea that closer things are more related than distant things, known as Tobler's first law of geography', is fundamental to understanding many spatial processes. If this concept applies to volunteered geographic information (VGI), it could help to efficiently allocate tasks in citizen science campaigns and help to improve the overall quality of collected data. In this paper, we use classifications of satellite imagery by volunteers from around the world to test whether local familiarity with landscapes helps their performance. Our results show that volunteers identify cropland slightly better within their home country, and do slightly worse as a function of linear distance between their home and the location represented in an image. Volunteers with a professional background in remote sensing or land cover did no better than the general population at this task, but they did not show the decline with distance that was seen among other participants. Even in a landscape where pasture is easily confused for cropland, regional residents demonstrated no advantage. Where we did find evidence for local knowledge aiding classification performance, the realized impact of this effect was tiny. Rather, the inherent difficulty of a task is a much more important predictor of volunteer performance. These findings suggest that, at least for simple tasks, the geographical origin of VGI volunteers has little impact on their ability to complete image classifications.

Keywords

crowdsourcing; citizen science; data quality; Tobler's Law; local knowledge; remote sensing; land cover; cropland; volunteered geographical information

Published in

Remote Sensing
2016, volume: 8, number: 9, article number: 774
Publisher: MDPI AG

Authors' information

International Institute for Applied Systems Analysis (IIASA)
Swedish University of Agricultural Sciences, Southern Swedish Forest Research Centre
Sturn, Tobias
International Institute for Applied Systems Analysis (IIASA)
See, Linda
International Institute for Applied Systems Analysis (IIASA)
Fritz, Steffen
International Institute for Applied Systems Analysis (IIASA)

UKÄ Subject classification

Environmental Sciences
Geosciences, Multidisciplinary

Publication Identifiers

DOI: https://doi.org/10.3390/rs8090774

URI (permanent link to this page)

https://res.slu.se/id/publ/79984