  Accurate crop type maps are an essential source of information for monitoring
yield progress at scale, projecting global crop production, and planning
effective policies. To date, however, crop type maps remain challenging to
create in low and middle-income countries due to a lack of ground truth labels
for training machine learning models. Field surveys are the gold standard in
terms of accuracy but require an often-prohibitively large amount of time,
money, and statistical capacity. In recent years, street-level imagery, such as
Google Street View, KartaView, and Mapillary, has become available around the
world. Such imagery contains rich information about crop types grown at
particular locations and times. In this work, we develop an automated system to
generate crop type ground references using deep learning and Google Street View
imagery. The method efficiently curates a set of street view images containing
crop fields, trains a model to predict crop type by utilizing weakly-labelled
images from disparate out-of-domain sources, and combines predicted labels with
remote sensing time series to create a wall-to-wall crop type map. We show
that, in Thailand, the resulting country-wide map of rice, cassava, maize, and
sugarcane achieves an accuracy of 93%. We publicly release the first-ever crop
type map for all of Thailand 2022 at 10m-resolution with no gaps. To our
knowledge, this is the first time a 10m-resolution, multi-crop map has been
created for any smallholder country. As the availability of roadside imagery
expands, our pipeline provides a way to map crop types at scale around the
globe, especially in underserved smallholder regions.
