Optimizing Crowdsourced Land Use and Land Cover Data Collection: A Two-Stage Approach

Elena Moltchanova, Myroslava Lesiv, Linda See, Julie Mugford and Steffen Fritz
Additional contact information
Elena Moltchanova: School of Mathematics and Statistics, University of Canterbury, Christchurch 8041, New Zealand
Myroslava Lesiv: International Institute for Applied Systems Analysis (IIASA), 2361 Laxenburg, Austria
Linda See: International Institute for Applied Systems Analysis (IIASA), 2361 Laxenburg, Austria
Julie Mugford: School of Mathematics and Statistics, University of Canterbury, Christchurch 8041, New Zealand
Steffen Fritz: International Institute for Applied Systems Analysis (IIASA), 2361 Laxenburg, Austria

Land, 2022, vol. 11, issue 7, 1-15

Abstract: Citizen science has become an increasingly popular approach to scientific data collection, where classification tasks involving visual interpretation of images is one prominent area of application, e.g., to support the production of land cover and land-use maps. Achieving a minimum accuracy in these classification tasks at a minimum cost is the subject of this study. A Bayesian approach provides an intuitive and reasonably straightforward solution to achieve this objective. However, its application requires additional information, such as the relative frequency of the classes and the accuracy of each user. While the former is often available, the latter requires additional data collection. In this paper, we present a two-stage approach to gathering this additional information. We demonstrate its application using a hypothetical two-class example and then apply it to an actual crowdsourced dataset with five classes, which was taken from a previous Geo-Wiki crowdsourcing campaign on identifying the size of agricultural fields from very high-resolution satellite imagery. We also attach the R code for the implementation of the newly presented approach.

Keywords: citizen science; crowdsourcing; classification task; visual interpretation; earth observation; satellite imagery; Bayesian; cost optimization; Geo-Wiki; field size (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/11/7/958/pdf (application/pdf)
https://www.mdpi.com/2073-445X/11/7/958/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:11:y:2022:i:7:p:958-:d:843914

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().