| 来源类型 | Book Section |
| DOI | 10.1007/978-3-319-38884-7_13 |
| The Cropland Capture Game: good annotators versus vote aggregation methods. | |
| Fritz S; Khachay M; Nurmukhametov O; See L | |
| 发表日期 | 2016 |
| 出处 | Advanced Computational Methods for Knowledge Engineering - Proceedings of the 4th International Conference on Computer Science, Applied Mathematics and Applications, ICCSAMA 2016, 2-3 May, 2016, Vienna, Austria. pp. 167-180 Cham, Switzerland: Springer International Publishing. ISBN 978-3-319-38884-7 DOI: 10.1007/978-3-319-38884-7_13 . |
| 出版年 | 2016 |
| 语种 | 英语 |
| 摘要 | The Cropland Capture game, which is a recently developed Geo-Wiki game, aims to map cultivated lands using around 17,000 satellite images from the Earth’s surface. Using a perceptual hash and blur detection algorithm, we improve the quality of the Cropland Capture game’s dataset. We then benchmark state-of-the-art algorithms for an aggregation of votes using results of well-known machine learning algorithms as a baseline. We demonstrate that volunteer-image assignment is highly irregular and only good annotators are presented (there are no spammers and malicious voters). We conjecture that the last fact is the main reason for surprisingly similar accuracy levels across all examined algorithms. Finally, we increase the estimated consistency with expert opinion from 77 to 91 % and up to 96 % if we restrict our attention to images with more than 9 votes. |
| 主题 | Ecosystems Services and Management (ESM) |
| 关键词 | Crowdsourcing Image processing Votes aggregation |
| URL | http://pure.iiasa.ac.at/id/eprint/13204/ |
| 来源智库 | International Institute for Applied Systems Analysis (Austria) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/134488 |
| 推荐引用方式 GB/T 7714 | Fritz S,Khachay M,Nurmukhametov O,et al. The Cropland Capture Game: good annotators versus vote aggregation methods.. 2016. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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