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07/20/2023
Graphs in State-Space Models for Granger Causality in Climate Science
Granger causality (GC) is often considered not an actual form of causali...
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06/19/2023
TeleViT: Teleconnection-driven Transformers Improve Subseasonal to Seasonal Wildfire Forecasting
Wildfires are increasingly exacerbated as a result of climate change, ne...
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06/08/2023
Mesogeos: A multi-purpose dataset for data-driven wildfire modeling in the Mediterranean
We introduce Mesogeos, a large-scale multi-purpose dataset for wildfire ...
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05/21/2023
Discovering Causal Relations and Equations from Data
Physics is a field of science that has traditionally used the scientific...
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05/15/2023
Artificial intelligence to advance Earth observation: a perspective
Earth observation (EO) is a prime instrument for monitoring land and oce...
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05/03/2023
Understanding cirrus clouds using explainable machine learning
Cirrus clouds are key modulators of Earth's climate. Their dependencies ...
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05/18/2022
The Kernelized Taylor Diagram
This paper presents the kernelized Taylor diagram, a graphical framework...
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04/12/2022
Unsupervised Anomaly and Change Detection with Multivariate Gaussianization
Anomaly detection is a field of intense research. Identifying low probab...
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04/07/2022
Inference over radiative transfer models using variational and expectation maximization methods
Earth observation from satellites offers the possibility to monitor our ...
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12/21/2021
Deep Learning and Earth Observation to Support the Sustainable Development Goals
The synergistic combination of deep learning models and Earth observatio...
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11/29/2021
Graph Embedding via High Dimensional Model Representation for Hyperspectral Images
Learning the manifold structure of remote sensing images is of paramount...
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11/04/2021
Deep Learning Methods for Daily Wildfire Danger Forecasting
Wildfire forecasting is of paramount importance for disaster risk reduct...
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07/18/2021
Compressed particle methods for expensive models with application in Astronomy and Remote Sensing
In many inference problems, the evaluation of complex and costly models ...
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04/16/2021
Integrating Domain Knowledge in Data-driven Earth Observation with Process Convolutions
The modelling of Earth observation data is a challenging problem, typica...
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04/15/2021
Semisupervised Manifold Alignment of Multimodal Remote Sensing Images
We introduce a method for manifold alignment of different modalities (or...
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04/11/2021
Towards a Collective Agenda on AI for Earth Science Data Analysis
In the last years we have witnessed the fields of geosciences and remote...
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01/07/2021
Retrieval of Coloured Dissolved Organic Matter with Machine Learning Methods
The coloured dissolved organic matter (CDOM) concentration is the standa...
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12/22/2020
Learning Structures in Earth Observation Data with Gaussian Processes
Gaussian Processes (GPs) has experienced tremendous success in geoscienc...
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12/11/2020
A Methodology to Derive Global Maps of Leaf Traits Using Remote Sensing and Climate Data
This paper introduces a modular processing chain to derive global high-r...
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12/11/2020
Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes
In this work we evaluate multi-output (MO) Gaussian Process (GP) models ...
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12/11/2020
Synergistic Integration of Optical and Microwave Satellite Data for Crop Yield Estimation
Developing accurate models of crop stress, phenology and productivity is...
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12/09/2020
Convolutional Neural Networks for Multispectral Image Cloud Masking
Convolutional neural networks (CNN) have proven to be state of the art m...
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12/09/2020
Disentangling Derivatives, Uncertainty and Error in Gaussian Process Models
Gaussian Processes (GPs) are a class of kernel methods that have shown t...
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12/09/2020
Consistent regression of biophysical parameters with kernel methods
This paper introduces a novel statistical regression framework that allo...
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12/09/2020
Kernel Anomalous Change Detection for Remote Sensing Imagery
Anomalous change detection (ACD) is an important problem in remote sensi...
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12/09/2020
Spatial noise-aware temperature retrieval from infrared sounder data
In this paper we present a combined strategy for the retrieval of atmosp...
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12/08/2020
Retrieval of Case 2 Water Quality Parameters with Machine Learning
Water quality parameters are derived applying several machine learning r...
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12/07/2020
Causal Inference in Geoscience and Remote Sensing from Observational Data
Establishing causal relations between random variables from observationa...
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12/07/2020
Efficient Nonlinear RX Anomaly Detectors
Current anomaly detection algorithms are typically challenged by either ...
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12/07/2020
Causal Inference in Geosciences with Kernel Sensitivity Maps
Establishing causal relations between random variables from observationa...
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12/07/2020
Deep Gaussian Processes for geophysical parameter retrieval
This paper introduces deep Gaussian processes (DGPs) for geophysical par...
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12/07/2020
Retrieval of aboveground crop nitrogen content with a hybrid machine learning method
Hyperspectral acquisitions have proven to be the most informative Earth ...
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12/07/2020
Randomized kernels for large scale Earth observation applications
Dealing with land cover classification of the new image sources has also...
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12/07/2020
Physics-Aware Gaussian Processes in Remote Sensing
Earth observation from satellite sensory data poses challenging problems...
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12/07/2020
Mapping Leaf Area Index with a Smartphone and Gaussian Processes
Leaf area index (LAI) is a key biophysical parameter used to determine f...
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12/07/2020
Gradient-based Automatic Look-Up Table Generator for Atmospheric Radiative Transfer Models
Atmospheric correction of Earth Observation data is one of the most crit...
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12/07/2020
Emulation as an Accurate Alternative to Interpolation in Sampling Radiative Transfer Codes
Computationally expensive Radiative Transfer Models (RTMs) are widely us...
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12/07/2020
Spectral band selection for vegetation properties retrieval using Gaussian processes regression
With current and upcoming imaging spectrometers, automated band analysis...
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12/07/2020
Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval
Kernel-based machine learning regression algorithms (MLRAs) are potentia...
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12/07/2020
Multi-temporal and multi-source remote sensing image classification by nonlinear relative normalization
Remote sensing image classification exploiting multiple sensors is a ver...
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12/06/2020
Understanding Climate Impacts on Vegetation with Gaussian Processes in Granger Causality
Global warming is leading to unprecedented changes in our planet, with g...
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12/05/2020
Fusing Optical and SAR time series for LAI gap filling with multioutput Gaussian processes
The availability of satellite optical information is often hampered by t...
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12/02/2020
Information Theory in Density Destructors
Density destructors are differentiable and invertible transforms that ma...
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11/17/2020
Learning drivers of climate-induced human migrations with Gaussian processes
In the current context of climate change, extreme heatwaves, droughts, a...
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10/18/2020
Living in the Physics and Machine Learning Interplay for Earth Observation
Most problems in Earth sciences aim to do inferences about the system, w...
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10/13/2020
Gaussianizing the Earth: Multidimensional Information Measures for Earth Data Analysis
Information theory is an excellent framework for analyzing Earth system ...
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10/08/2020
Information Theory Measures via Multidimensional Gaussianization
Information theory is an outstanding framework to measure uncertainty, d...
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07/29/2020
Kernel Methods and their derivatives: Concept and perspectives for the Earth system sciences
Kernel methods are powerful machine learning techniques which implement ...
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07/02/2020
A Perspective on Gaussian Processes for Earth Observation
Earth observation (EO) by airborne and satellite remote sensing and in-s...
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05/20/2020