106 citations · 297 across the 8 of their papers we have counts for
8 papers
Inference over radiative transfer models using variational and expectation maximization methods
Daniel Heestermans Svendsen, Daniel Hernández-Lobato, Luca Martino +3
Earth observation from satellites offers the possibility to monitor our planet with unprecedented accuracy. Radiative transfer models (RTMs) encode the energy transfer through the…
Deep Gaussian Processes for Biogeophysical Parameter Retrieval and Model Inversion
Daniel Heestermans Svendsen, Pablo Morales-Alvarez, Ana Belen Ruescas +2
Parameter retrieval and model inversion are key problems in remote sensing and Earth observation. Currently, different approximations exist: a direct, yet costly, inversion of radi…
Integrating Domain Knowledge in Data-driven Earth Observation with Process Convolutions
Daniel Heestermans Svendsen, Maria Piles, Jordi Muñoz-Marí +3
The modelling of Earth observation data is a challenging problem, typically approached by either purely mechanistic or purely data-driven methods. Mechanistic models encode the dom…
Deep Gaussian Processes for geophysical parameter retrieval
Daniel Heestermans Svendsen, Pablo Morales-Álvarez, Rafael Molina +1
This paper introduces deep Gaussian processes (DGPs) for geophysical parameter retrieval. Unlike the standard full GP model, the DGP accounts for complicated (modular, hierarchical…
Physics-Aware Gaussian Processes in Remote Sensing
Gustau Camps-Valls, Luca Martino, Daniel H. Svendsen +5
Earth observation from satellite sensory data poses challenging problems, where machine learning is currently a key player. In recent years, Gaussian Process (GP) regression has ex…
Living in the Physics and Machine Learning Interplay for Earth Observation
Gustau Camps-Valls, Daniel H. Svendsen, Jordi Cortés-Andrés +7
Most problems in Earth sciences aim to do inferences about the system, where accurate predictions are just a tiny part of the whole problem. Inferences mean understanding variables…