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

18098 9 98224 2 302081 document 98224 land-11-00958-v2.pdf application/pdf 410abde424e63d09d934b10c724feae2 MD5 1611965 2022-07-05 11:50:51 https://pure.iiasa.ac.at/id/eprint/18098/1/land-11-00958-v2.pdf 18098 1 1 application/pdf text en public cc_by_4

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published 98225 1 302087 document 98225 indexcodes.txt text/x-c++ 06e39cb41d84daa0a41b8f12e94e6b8f MD5 11141 2022-07-05 12:00:52 https://pure.iiasa.ac.at/id/eprint/18098/2/indexcodes.txt 18098 2 2 text/x-c++ other Generate index codes conversion from text to indexcodes en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/98224 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/98224 http://eprints.org/relation/issmallThumbnailVersionOf https://pure.iiasa.ac.at/id/document/98224 archive 353 disk0/00/01/80/98 2022-07-05 11:52:27 2025-02-21 12:04:21 2022-07-05 11:52:27 article show Moltchanova E. 7915 Lesiv M. 8477 0000-0001-9846-3342 See L. 8571 0000-0002-2665-7065 Mugford J. Fritz S. 8130 0000-0003-0420-8549 Optimizing Crowdsourced Land Use and Land Cover Data Collection: A Two-Stage Approach pub ASA NODES SI citizen science; crowdsourcing; classification task; visual interpretation; earth observation; satellite imagery; Bayesian; cost optimization; Geo-Wiki; field size 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. 2022-06-21 published MDPI 10.3390/land11070958 1460 181 276 98 public Land 11 7 e958 TRUE 2073-445X Geo-Wiki no info:eu-repo/semantics/article