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Found 6 papers, 1 papers with code
Integrating Domain Knowledge in Data-driven Earth Observation with Process Convolutions
We specifically propose the use of a class of GP convolution models called latent force models (LFMs) for EO time series modelling, analysis and understanding.
Synergistic Integration of Optical and Microwave Satellite Data for Crop Yield Estimation
Developing accurate models of crop stress, phenology and productivity is of paramount importance, given the increasing need of food.
Nonlinear Distribution Regression for Remote Sensing Applications
When the target variable is available at a resolution that matches the remote sensing observations, standard algorithms such as neural networks, random forests or Gaussian processes are readily available to relate the two.
Understanding Climate Impacts on Vegetation with Gaussian Processes in Granger Causality
Global warming is leading to unprecedented changes in our planet, with great societal, economical and environmental implications, especially with the growing demand of biofuels and food.
Living in the Physics and Machine Learning Interplay for Earth Observation
Most problems in Earth sciences aim to do inferences about the system, where accurate predictions are just a tiny part of the whole problem.
Gaussianizing the Earth: Multidimensional Information Measures for Earth Data Analysis
Information theory is an excellent framework for analyzing Earth system data because it allows us to characterize uncertainty and redundancy, and is universally interpretable.
