Combining satellite imagery with machine learning (SIML) has the potential to
address global challenges by remotely estimating socioeconomic and
environmental conditions in data-poor regions, yet the resource requirements of
SIML limit its accessibility and use. We show that a single encoding of
satellite imagery can generalize across diverse prediction tasks (e.g. forest
cover, house price, road length). Our method achieves accuracy competitive with
deep neural networks at orders of magnitude lower computational cost, scales
globally, delivers label super-resolution predictions, and facilitates
characterizations of uncertainty. Since image encodings are shared across
tasks, they can be centrally computed and distributed to unlimited researchers,
who need only fit a linear regression to their own ground truth data in order
to achieve state-of-the-art SIML performance.

使用卫星图像与机器学习相结合，可以远程估计数据贫乏地区的社会经济和环境条件，而这种方法可能会受到资源需求的限制。该研究表明，一种单一的卫星图像编码可以概括各种预测任务的要求，如林地覆盖、房价和道路长度。该方法不仅能够在成本低的情况下达到与深度神经网络相同的准确率，而且同时具有全球扩展性、标签分辨率和不确定性特性，因此实现成果共享。