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来源类型Article
规范类型其他
DOI10.3390/rs8090774
Local knowledge and professional background have a minimal impact on volunteer citizen science performance in a land-cover classification task.
Salk C; Sturn T; See L; Fritz S
发表日期2016
出处Remote Sensing 8 (10): e774
出版年2016
语种英语
摘要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.
主题Ecosystems Services and Management (ESM)
URLhttp://pure.iiasa.ac.at/id/eprint/13974/
来源智库International Institute for Applied Systems Analysis (Austria)
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条目标识符http://119.78.100.153/handle/2XGU8XDN/130669
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Salk C,Sturn T,See L,et al. Local knowledge and professional background have a minimal impact on volunteer citizen science performance in a land-cover classification task.. 2016.
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