论文标题
具有全球卫星图像的可通用且可访问的机器学习方法
A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery
论文作者
论文摘要
将卫星图像与机器学习（SIML）相结合，有可能通过远程估计数据贫困地区的社会经济和环境条件来应对全球挑战，但是SIML的资源要求限制了其可访问性和使用。我们表明，卫星图像的单个编码可以跨越各种预测任务（例如森林覆盖，房价，道路长度）。我们的方法以较低的计算成本，全球范围，提供标签的超分辨率预测，并促进不确定性的特征，从而在较低的计算成本下以较低的计算成本，尺度降低计算成本，从而实现准确的竞争。由于图像编码是在各个任务之间共享的，因此可以将它们集中计算并分发给无限的研究人员，他们只需要将线性回归拟合到自己的地面真相数据，以实现最新的SIML性能。
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.
