Rating crowdsourced annotations: evaluating contributions of variable quality and completeness

作     者：Giles M.Foody 

作者机构：School of GeographyUniversity of NottinghamNottinghamUK 

出 版 物：《International Journal of Digital Earth》 (国际数字地球学报（英文）)

年 卷 期：2014年第7卷第8期

页      面：650-670页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

基　　金：This work benefited in part from funding from the Engineering and Physical Science Research Council[grant number EP/J0020230/1] the British Academy[grant number SG112788] 

主　　题：volunteered geographic information accuracy error 

摘      要：Crowdsourcing has become a popular means to acquire data about the Earth and its environment inexpensively,but the data-sets obtained are typically imperfect and of unknown *** common imperfections with crowdsourced data are the contributions from cheats or spammers and missing *** effect of the latter two imperfections on a method to evaluate the accuracy of crowdsourced data via a latent class model was *** simulated and real data-sets,it was shown that the method is able to derive useful information on the accuracy of crowdsourced data even when the degree of imperfection was very *** practical potential of this ability to obtain accuracy information within the geospatial sciences and the realm of Digital Earth applications was indicated with reference to an evaluation of building damage maps produced by multiple bodies after the 2010 earthquake in ***,the method allowed data-sets to be ranked in approximately the correct order of accuracy and this could help ensure that the most appropriate data-sets are used.