eprintid: 13974 rev_number: 17 eprint_status: archive userid: 5 dir: disk0/00/01/39/74 datestamp: 2016-11-18 10:43:13 lastmod: 2022-10-19 05:00:41 status_changed: 2016-11-18 10:43:13 type: article metadata_visibility: show creators_name: Salk, C. creators_name: Sturn, T. creators_name: See, L. creators_name: Fritz, S. creators_id: 8556 creators_id: 2051 creators_id: 8571 creators_id: 8130 creators_orcid: 0000-0002-2665-7065 creators_orcid: 0000-0003-0420-8549 title: Local knowledge and professional background have a minimal impact on volunteer citizen science performance in a land-cover classification task ispublished: pub divisions: prog_esm 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. date: 2016-09 date_type: published publisher: MDPI AG id_number: doi:10.3390/rs8090774 creators_browse_id: 262 creators_browse_id: 301 creators_browse_id: 276 creators_browse_id: 98 full_text_status: public publication: Remote Sensing volume: 8 number: 10 pagerange: e774 refereed: TRUE issn: 2072-4292 projects: Harnessing the power of crowdsourcing to improve land cover and land-use information (CROWDLAND, FP7 617754) coversheets_dirty: FALSE fp7_project: yes fp7_project_id: info:eu-repo/grantAgreement/EC/FP7/617754/EU/Harnessing the power of crowdsourcing to improve land cover and land-use information/CROWDLAND fp7_type: info:eu-repo/semantics/article access_rights: info:eu-repo/semantics/openAccess citation: Salk, C. , Sturn, T. , See, L. ORCID: https://orcid.org/0000-0002-2665-7065 , & Fritz, S. ORCID: https://orcid.org/0000-0003-0420-8549 (2016). Local knowledge and professional background have a minimal impact on volunteer citizen science performance in a land-cover classification task. Remote Sensing 8 (10) e774. 10.3390/rs8090774 . document_url: https://pure.iiasa.ac.at/id/eprint/13974/1/remotesensing-08-00905.pdf