标题:高斯地球观测过程的透视

摘要】通过机载和卫星遥感进行的地球观测(EO)和原位观测在监测我们的星球方面发挥着根本作用。在过去的十年中,机器学习和高斯过程(GPs)在以时间分辨的方式从本地和全球范围内获取的图像估计生物地球物理变量方面取得了卓越的成果。 GP不仅为预测提供准确的估计值,而且还提供原则上的不确定性估计值,可以轻松容纳来自不同传感器和多时相采集的多峰数据,允许引入物理知识,并对不确定性量化和误差传播进行正式处理。尽管在正向和逆向建模方面取得了长足的进步,但是GP模型仍然必须面对重要的挑战,本透视文件对此进行了修订。 GP模型应朝着数据驱动的,物理感知的模型发展,该模型应尊重信号特征,与物理基本定律保持一致,并从纯回归转向观察性因果推理。

Title: A Perspective on Gaussian Processes for Earth Observation

[abstract]  Earth observation (EO) by airborne and satellite remote sensing and in-situ observations play a fundamental role in monitoring our planet. In the last decade, machine learning and Gaussian processes (GPs) in particular has attained outstanding results in the estimation of bio-geo-physical variables from the acquired images at local and global scales in a time-resolved manner. GPs provide not only accurate estimates but also principled uncertainty estimates for the predictions, can easily accommodate multimodal data coming from different sensors and from multitemporal acquisitions, allow the introduction of physical knowledge, and a formal treatment of uncertainty quantification and error propagation. Despite great advances in forward and inverse modelling, GP models still have to face important challenges that are revised in this perspective paper. GP models should evolve towards data-driven physics-aware models that respect signal characteristics, be consistent with elementary laws of physics, and move from pure regression to observational causal inference.

【作者】Gustau Camps-Valls, Dino Sejdinovic, Jakob Runge, Markus Reichstein

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