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%. 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.

我们开发了一种利用深度学习和 Google 街景图像生成作物类型地面参考的自动化系统，能够通过利用异源图像从不同领域预测作物类型，并将预测的标签与遥感时序图像相结合，从而在泰国获得 91% 的精确度，从而实现了在全球范围内扩展地图作物类型的目的，特别是在未受服务的小农户地区。