  Spatial information on cropland distribution, often called cropland or crop
maps, are critical inputs for a wide range of agriculture and food security
analyses and decisions. However, high-resolution cropland maps are not readily
available for most countries, especially in regions dominated by smallholder
farming (e.g., sub-Saharan Africa). These maps are especially critical in times
of crisis when decision makers need to rapidly design and enact
agriculture-related policies and mitigation strategies, including providing
humanitarian assistance, dispersing targeted aid, or boosting productivity for
farmers. A major challenge for developing crop maps is that many regions do not
have readily accessible ground truth data on croplands necessary for training
and validating predictive models, and field campaigns are not feasible for
collecting labels for rapid response. We present a method for rapid mapping of
croplands in regions where little to no ground data is available. We present
results for this method in Togo, where we delivered a high-resolution (10 m)
cropland map in under 10 days to facilitate rapid response to the COVID-19
pandemic by the Togolese government. This demonstrated a successful transition
of machine learning applications research to operational rapid response in a
real humanitarian crisis. All maps, data, and code are publicly available to
enable future research and operational systems in data-sparse regions.
