A platform for crowdsourcing the creation of representative, accurate landcover maps. (June 2016)

• Record Type: Journal Article
• Authors: Estes, L.D.; McRitchie, D.; Choi, J.; Debats, S.; Evans, T.; Guthe, W.; Luo, D.; Ragazzo, G.; Zempleni, R.; Caylor, K.K.
• Is Part Of: Environmental modelling & software. Volume 80(2016:Jun.)
• Page Start: 41
• Page End: 53
• Publication Date: 2016-06
• Subjects: Remote sensing -- Landcover -- Crowd-sourcing -- Accuracy assessment -- Representative sampling -- Object extraction; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.01.011
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Accurate landcover maps are fundamental to understanding socio-economic and environmental patterns and processes, but existing datasets contain substantial errors. Crowdsourcing map creation may substantially improve accuracy, particularly for discrete cover types, but the quality and representativeness of crowdsourced data is hard to verify. We present an open-sourced platform, DIYlandcover, that serves representative samples of high resolution imagery to an online job market, where workers delineate individual landcover features of interest. Worker mapping skill is frequently assessed, providing estimates of overall map accuracy and a basis for performance-based payments. A trial of DIYlandcover showed that novice workers delineated South African cropland with 91% accuracy, exceeding the accuracy of current generation global landcover products, while capturing important geometric data. A scaling-up assessment suggests the possibility of developing an Africa-wide vector-based dataset of croplands for $2–3 million within 1.2–3.8 years. DIYlandcover can be readily adapted to map other discrete cover types. Highlights: A crowdsourcing platform that uses human pattern recognition skill to create accurate, geometrically rich landcover maps. Primary features: representative sampling, worker-specific accuracy assessment, and connection to online job markets. A cropland mapping trial showed 91% accuracy, and potential to make an Africa-wide map for $2–3 million within 1.2–3.8 years.