A platform for crowdsourcing the creation of representative, accurate landcover maps

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Lyndon D. Estes, Dennis McRitchie, Jonathan Choi, Stephanie R. Debats, Tom Evans, William Guthe, Dee Luo, Gabrielle Ragazzo, Reka Zempleni, Kelly K. Caylor

Published: 01 Jan 2016, Last Modified: 07 Nov 2024Environ. Model. Softw. 2016EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: 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.