510 citations · 3.4k across the 76 of their papers we have counts for
9 papers · 1 filter
Deep Learning and Earth Observation to Support the Sustainable Development Goals
Claudio Persello, Jan Dirk Wegner, Ronny Hänsch +4
The synergistic combination of deep learning models and Earth observation promises significant advances to support the sustainable development goals (SDGs). New developments and a…
Graph Embedding via High Dimensional Model Representation for Hyperspectral Images
Gulsen Taskin, Gustau Camps-Valls
Learning the manifold structure of remote sensing images is of paramount relevance for modeling and understanding processes, as well as to encapsulate the high dimensionality in a…
Deep Learning Methods for Daily Wildfire Danger Forecasting
Ioannis Prapas, Spyros Kondylatos, Ioannis Papoutsis +5
Wildfire forecasting is of paramount importance for disaster risk reduction and environmental sustainability. We approach daily fire danger prediction as a machine learning task, u…
Compressed particle methods for expensive models with application in Astronomy and Remote Sensing
Luca Martino, Víctor Elvira, Javier López-Santiago +1
In many inference problems, the evaluation of complex and costly models is often required. In this context, Bayesian methods have become very popular in several fields over the las…
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…