Conflation of expert and crowd reference data to validate global binary thematic maps
作者: François WaldnerAnne SchucknechtMyroslava LesivJavier GallegoLinda SeeAna Pérez-HoyosRaphaël d'AndrimontThomas de MaetJuan Carlos Laso BayasSteffen FritzOlivier LeoHervé KerdilesMónica DíezKristof Van TrichtSven GilliamsAndrii ShelestovMykola LavreniukMargareth SimõesRodrigo FerrazBeatriz BellónAgnès BéguéGerard HazeuVaclav StonacekJan KolomaznikJan MisurecSantiago R. VerónDiego de AbelleyraDmitry PlotnikovLi MingyongMrinal SinghaPrashant PatilMiao ZhangPierre Defourny
作者单位: 1Université Catholique de Louvain, Earth and Life Institute, Louvain-la-Neuve, Belgium
2Commonwealth Scientific and Industrial Research Organisation, Agriculture and Food, St Lucia, Australia
3European Commission Joint Research Centre, Ispra, Italy
4Karlsruhe Institute of Technology, Garmisch-Partenkirchen, Germany
5International Institute for Applied Systems Analysis, Laxenburg, Austria
6DEIMOS IMAGING, Boecillo, Valladolid, Spain
7VITO Remote Sensing, Mol, Belgium
8National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institue”, Kyiv, Ukraine
9Embrapa Solos, Rio de Janeiro, Brazil
10CIRAD, UMR Tetis, Montpellier, France
11Wageningen Environmental Research (Alterra), Wageningen, the Netherlands
12Gisat s.r.o., Prague, Czech Republic
13Instituto Nacional de Tecnología Agropecuaria (INTA), Hurlingham, Argentina
14Universidad de Buenos Aires and CONICET, Buenos Aires, Argentina
15Terrestrial Ecosystems Monitoring Laboratory, Space Research Institute of Russian Academy of Sciences (IKI), Moscow, Russia
16Key Laboratory of Digital Earth Science, Institude of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China
17Universidade do Estado do Rio de Janeiro – UERJ/FEN/DESC/PPGMA, Brazil
18Tetis, CIRAD, IRSTEA, AgroParisTech, CNRS, Univ Montpellier, Montpellier, France
刊名: Remote Sensing of Environment, 2019, Vol.221 , pp.235-246
来源数据库: Elsevier Journal
DOI: 10.1016/j.rse.2018.10.039
关键词: Accuracy assessmentCrowdsourcingVolunteered geographic informationData qualityStratified systematic samplingPhoto-interpretation
原始语种摘要: Abstract(#br)With the unprecedented availability of satellite data and the rise of global binary maps, the collection of shared reference data sets should be fostered to allow systematic product benchmarking and validation. Authoritative global reference data are generally collected by experts with regional knowledge through photo-interpretation. During the last decade, crowdsourcing has emerged as an attractive alternative for rapid and relatively cheap data collection, beckoning the increasingly relevant question: can these two data sources be combined to validate thematic maps? In this article, we compared expert and crowd data and assessed their relative agreement for cropland identification, a land cover class often reported as difficult to map. Results indicate that observations...
全文获取路径: Elsevier  (合作)
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影响因子:5.103 (2012)

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