Smallholder farms dominate in many parts of the world, particularly Sub-Saharan Africa.These systems are characterized by small, heterogeneous, and often indistinct field patterns, requiring a specialized methodology to map agricultural land cover. Using a variety of sites in South Africa, we present a new approach to mapping agricultural fields, based on efficient extraction of a vast set of simple, highly correlated, and interdependent features, followed by a random forest classifier. We achieved similar high performance across agricultural types, including the spectrally indistinct smallholder fields as well as the more easily distinguishable commercial fields, and demonstrated the ability to generalize performance across large geographic areas. In sensitivity analyses, we determined multi-temporal information provided greater gains in performance than the addition of multi-spectral bands available in DigitalGlobe Worldview-2 imagery.

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Highlights• DIYlandcover crowdsources the generation of landcover data, using human pattern recognition skill to create accurate maps with rich geometric detail.• It incorporates representative sampling and worker-specific accuracy assessment protocols, and connects to a large online job market. This design addresses three problems with crowdsourced mapping: representativity; data reliability; product delivery speed.• In a trial case, South African cropland was mapped with 91% accuracy by novice workers. A scaling up analysis found that an Africa-wide cropland map could potentially be developed using this software for $2-3 million within 1.2-3.8 years. AbstractAccurate 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 representa- * Corresponding author

Smallholder farms dominate in many parts of the world, particularly Sub-Saharan Africa. These systems are characterized by small, heterogeneous, and often indistinct field patterns, requiring a specialized methodology to map agricultural land cover. Using a variety of sites in South Africa, we present a new approach to mapping agricultural fields, based on efficient extraction of a vast set of simple, highly correlated, and interdependent features, followed by a random forest classifier. We achieved similar high performance across agricultural types, including the spectrally indistinct smallholder fields as well as the more easily distinguishable commercial fields, and demonstrated the ability to generalize performance across large geographic areas. In sensitivity analyses, we determined multi-temporal information provided greater gains in accuracy than multi-spectral information.

Highlights• DIYlandcover crowdsources the generation of landcover data, using human pattern recognition skill to create accurate maps with rich geometric detail.• It incorporates representative sampling and worker-specific accuracy assessment protocols, and connects to a large online job market. This design addresses three problems with crowdsourced mapping: representativity; data reliability; product delivery speed.• In a trial case, South African cropland was mapped with 91% accuracy by novice workers. A scaling up analysis found that an Africa-wide cropland map could potentially be developed using this software for $2-3 million within 1.2-3.8 years. AbstractAccurate 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 representa- * Corresponding author