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.

翻译：准确的作物类型地图是规模化监测产量进展、预测全球作物产量及制定有效政策的重要信息来源。然而，迄今为止，在中低收入国家，由于缺乏用于训练机器学习模型的地面真实标签，作物类型地图的绘制仍面临挑战。实地调查在准确性方面是黄金标准，但通常需要大量时间、资金和统计能力，这往往使其难以实施。近年来，街景影像（如Google街景、KartaView和Mapillary）已在全球范围内可用。此类影像包含特定地点和时间种植作物类型的丰富信息。在本研究中，我们开发了一个自动化系统，利用深度学习和Google街景影像生成作物类型的地面参考。该方法高效地整理了一组包含农田的街景图像，通过利用来自不同领域外来源的弱标记图像训练模型预测作物类型，并将预测标签与遥感时间序列数据结合，生成全覆盖的作物类型地图。我们证明，在泰国，所得的水稻、木薯、玉米和甘蔗全国性地图的准确率达到93%。随着路边影像可用性的扩展，我们的流程提供了一种在全球范围内规模化绘制作物类型地图的方法，尤其是在服务不足的小农地区。