786 citations · 5.5k across the 92 of their papers we have counts for
96 papers
TeleViT: Teleconnection-driven Transformers Improve Subseasonal to Seasonal Wildfire Forecasting
Ioannis Prapas, Nikolaos Ioannis Bountos, Spyros Kondylatos +3
Wildfires are increasingly exacerbated as a result of climate change, necessitating advanced proactive measures for effective mitigation. It is important to forecast wildfires week…
Graphs in State-Space Models for Granger Causality in Climate Science
Víctor Elvira, Émilie Chouzenoux, Jordi Cerdà +1
Granger causality (GC) is often considered not an actual form of causality. Still, it is arguably the most widely used method to assess the predictability of a time series from ano…
Mesogeos: A multi-purpose dataset for data-driven wildfire modeling in the Mediterranean
Spyros Kondylatos, Ioannis Prapas, Gustau Camps-Valls +1
We introduce Mesogeos, a large-scale multi-purpose dataset for wildfire modeling in the Mediterranean. Mesogeos integrates variables representing wildfire drivers (meteorology, veg…
Understanding cirrus clouds using explainable machine learning
Kai Jeggle, David Neubauer, Gustau Camps-Valls +1
Cirrus clouds are key modulators of Earth's climate. Their dependencies on meteorological and aerosol conditions are among the largest uncertainties in global climate models. This…
Discovering Causal Relations and Equations from Data
Gustau Camps-Valls, Andreas Gerhardus, Urmi Ninad +7
Physics is a field of science that has traditionally used the scientific method to answer questions about why natural phenomena occur and to make testable models that explain the p…
Let's consider more general nonlinear approaches to study teleconnections of climate variables
D. Bueso, M. Piles, G. Camps-Valls
The recent work by (Rieger et al 2021) is concerned with the problem of extracting features from spatio-temporal geophysical signals. The authors introduce the complex rotated MCA…
The Kernelized Taylor Diagram
Kristoffer Wickstrøm, J. Emmanuel Johnson, Sigurd Løkse +4
This paper presents the kernelized Taylor diagram, a graphical framework for visualizing similarities between data populations. The kernelized Taylor diagram builds on the widely u…
Machine-learned cloud classes from satellite data for process-oriented climate model evaluation
A. Kaps, A. Lauer, G. Camps-Valls +3
Clouds play a key role in regulating climate change but are difficult to simulate within Earth system models (ESMs). Improving the representation of clouds is one of the key tasks…
Unsupervised Anomaly and Change Detection with Multivariate Gaussianization
José A. Padrón-Hidalgo, Valero Laparra, Gustau Camps-Valls
Anomaly detection is a field of intense research. Identifying low probability events in data/images is a challenging problem given the high-dimensionality of the data, especially w…
Persistence in Complex Systems
S. Salcedo-Sanz, D. Casillas-Pérez, J. Del Ser +4
Persistence is an important characteristic of many complex systems in nature, related to how long the system remains at a certain state before changing to a different one. The stud…
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 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…
Semisupervised Manifold Alignment of Multimodal Remote Sensing Images
Devis Tuia, Michele Volpi, Maxime Trolliet +1
We introduce a method for manifold alignment of different modalities (or domains) of remote sensing images. The problem is recurrent when a set of multitemporal, multisource, multi…
Towards a Collective Agenda on AI for Earth Science Data Analysis
Devis Tuia, Ribana Roscher, Jan Dirk Wegner +3
In the last years we have witnessed the fields of geosciences and remote sensing and artificial intelligence to become closer. Thanks to both the massive availability of observatio…
Retrieval of Coloured Dissolved Organic Matter with Machine Learning Methods
Ana B. Ruescas, Martin Hieronymi, Sampsa Koponen +2
The coloured dissolved organic matter (CDOM) concentration is the standard measure of humic substance in natural waters. CDOM measurements by remote sensing is calculated using the…
Learning Structures in Earth Observation Data with Gaussian Processes
Fernando Mateo, Jordi Munoz-Mari, Valero Laparra +2
Gaussian Processes (GPs) has experienced tremendous success in geoscience in general and for bio-geophysical parameter retrieval in the last years. GPs constitute a solid Bayesian…
A global Canopy Water Content product from AVHRR/Metop
Francisco Javier García-Haro, Manuel Campos-Taberner, Álvaro Moreno +8
Spatially and temporally explicit canopy water content (CWC) data are important for monitoring vegetation status, and constitute essential information for studying ecosystem-climat…
Interpolation and Gap Filling of Landsat Reflectance Time Series
Alvaro Moreno-Martinez, Marco Maneta, Gustau Camps-Valls +4
Products derived from a single multispectral sensor are hampered by a limited spatial, spectral or temporal resolutions. Image fusion in general and downscaling/blending in particu…
A Methodology to Derive Global Maps of Leaf Traits Using Remote Sensing and Climate Data
Alvaro Moreno-Martinez, Gustau Camps-Valls, Jens Kattge +19
This paper introduces a modular processing chain to derive global high-resolution maps of leaf traits. In particular, we present global maps at 500 m resolution of specific leaf ar…