A Perspective on Gaussian Processes for Earth Observation
- URL: http://arxiv.org/abs/2007.01238v1
- Date: Thu, 2 Jul 2020 16:44:11 GMT
- Title: A Perspective on Gaussian Processes for Earth Observation
- Authors: Gustau Camps-Valls and Dino Sejdinovic and Jakob Runge and Markus
Reichstein
- Abstract summary: Earth observation by airborne and satellite remote sensing and in-situ observations play a fundamental role in monitoring our planet.
Machine learning and Gaussian processes (GPs) in particular has attained outstanding results in the estimation of bio-geo-physical variables.
Despite great advances in forward and inverse modelling, GP models still have to face important challenges.
- Score: 23.66931064985429
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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