2 code implementations • 22 Mar 2024 • Zhitong Xiong, Yi Wang, Fahong Zhang, Adam J. Stewart, Joëlle Hanna, Damian Borth, Ioannis Papoutsis, Bertrand Le Saux, Gustau Camps-Valls, Xiao Xiang Zhu

The development of foundation models has revolutionized our ability to interpret the Earth's surface using satellite observational data.

1 code implementation • 21 Mar 2024 • Nathan Mankovich, Homer Durand, Emiliano Diaz, Gherardo Varando, Gustau Camps-Valls

Detecting latent confounders from proxy variables is an essential problem in causal effect estimation.

no code implementations • 13 Mar 2024 • Shan Zhao, Ioannis Prapas, Ilektra Karasante, Zhitong Xiong, Ioannis Papoutsis, Gustau Camps-Valls, Xiao Xiang Zhu

In that direction, we propose integrating causality with Graph Neural Networks (GNNs) that explicitly model the causal mechanism among complex variables via graph learning.

no code implementations • 4 Mar 2024 • Homer Durand, Gherardo Varando, Nathan Mankovich, Gustau Camps-Valls

We introduce a causal regularisation extension to anchor regression (AR) for improved out-of-distribution (OOD) generalisation.

no code implementations • 20 Feb 2024 • Kai-Hendrik Cohrs, Gherardo Varando, Nuno Carvalhais, Markus Reichstein, Gustau Camps-Valls

Hybrid modeling integrates machine learning with scientific knowledge to enhance interpretability, generalization, and adherence to natural laws.

1 code implementation • 8 Jan 2024 • Nathan Mankovich, Gustau Camps-Valls, Tolga Birdal

In this work, we present a unifying formalism for PCA and its variants, and introduce a framework based on the flags of linear subspaces, ie a hierarchy of nested linear subspaces of increasing dimension, which not only allows for a common implementation but also yields novel variants, not explored previously.

no code implementations • 7 Dec 2023 • Ilias Tsoumas, Vasileios Sitokonstantinou, Georgios Giannarakis, Evagelia Lampiri, Christos Athanassiou, Gustau Camps-Valls, Charalampos Kontoes, Ioannis Athanasiadis

Pesticides serve as a common tool in agricultural pest control but significantly contribute to the climate crisis.

no code implementations • 17 Oct 2023 • Jordi Cerdà-Bautista, José María Tárraga, Vasileios Sitokonstantinou, Gustau Camps-Valls

In the face of climate change-induced droughts, vulnerable regions encounter severe threats to food security, demanding urgent humanitarian assistance.

no code implementations • 20 Jul 2023 • Víctor Elvira, Émilie Chouzenoux, Jordi Cerdà, Gustau Camps-Valls

Granger causality (GC) is often considered not an actual form of causality.

1 code implementation • 19 Jun 2023 • Ioannis Prapas, Nikolaos Ioannis Bountos, Spyros Kondylatos, Dimitrios Michail, Gustau Camps-Valls, Ioannis Papoutsis

To achieve such accurate long-term forecasts at a global scale, it is crucial to employ models that account for the Earth system's inherent spatio-temporal interactions, such as memory effects and teleconnections.

1 code implementation • NeurIPS 2023 • Spyros Kondylatos, Ioannis Prapas, Gustau Camps-Valls, Ioannis Papoutsis

We introduce Mesogeos, a large-scale multi-purpose dataset for wildfire modeling in the Mediterranean.

no code implementations • 21 May 2023 • Gustau Camps-Valls, Andreas Gerhardus, Urmi Ninad, Gherardo Varando, Georg Martius, Emili Balaguer-Ballester, Ricardo Vinuesa, Emiliano Diaz, Laure Zanna, Jakob Runge

Discovering equations, laws and principles that are invariant, robust and causal explanations of the world has been fundamental in physical sciences throughout the centuries.

no code implementations • 15 May 2023 • Devis Tuia, Konrad Schindler, Begüm Demir, Gustau Camps-Valls, Xiao Xiang Zhu, Mrinalini Kochupillai, Sašo Džeroski, Jan N. van Rijn, Holger H. Hoos, Fabio Del Frate, Mihai Datcu, Jorge-Arnulfo Quiané-Ruiz, Volker Markl, Bertrand Le Saux, Rochelle Schneider

Earth observation (EO) is a prime instrument for monitoring land and ocean processes, studying the dynamics at work, and taking the pulse of our planet.

1 code implementation • 3 May 2023 • Kai Jeggle, David Neubauer, Gustau Camps-Valls, Ulrike Lohmann

Their dependencies on meteorological and aerosol conditions are among the largest uncertainties in global climate models.

1 code implementation • 18 May 2022 • Kristoffer Wickstrøm, J. Emmanuel Johnson, Sigurd Løkse, Gustau Camps-Valls, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen

Our proposed kernelized Taylor diagram is capable of visualizing similarities between populations with minimal assumptions of the data distributions.

no code implementations • 12 Apr 2022 • José A. Padrón-Hidalgo, Valero Laparra, Gustau Camps-Valls

Identifying low probability events in data/images is a challenging problem given the high-dimensionality of the data, especially when no (or little) information about the anomaly is available a priori.

1 code implementation • 7 Apr 2022 • Daniel Heestermans Svendsen, Daniel Hernández-Lobato, Luca Martino, Valero Laparra, Alvaro Moreno, Gustau Camps-Valls

Radiative transfer models (RTMs) encode the energy transfer through the atmosphere, and are used to model and understand the Earth system, as well as to estimate the parameters that describe the status of the Earth from satellite observations by inverse modeling.

no code implementations • 29 Nov 2021 • 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 reduced set of informative features for subsequent classification, regression, or unmixing.

no code implementations • 4 Nov 2021 • Ioannis Prapas, Spyros Kondylatos, Ioannis Papoutsis, Gustau Camps-Valls, Michele Ronco, Miguel-Ángel Fernández-Torres, Maria Piles Guillem, Nuno Carvalhais

Wildfire forecasting is of paramount importance for disaster risk reduction and environmental sustainability.

no code implementations • 18 Jul 2021 • Luca Martino, Víctor Elvira, Javier López-Santiago, Gustau Camps-Valls

In many inference problems, the evaluation of complex and costly models is often required.

no code implementations • 16 Apr 2021 • Daniel Heestermans Svendsen, Maria Piles, Jordi Muñoz-Marí, David Luengo, Luca Martino, Gustau Camps-Valls

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.

no code implementations • 16 Apr 2021 • Daniel Heestermans Svendsen, Pablo Morales-Alvarez, Ana Belen Ruescas, Rafael Molina, Gustau Camps-Valls

Currently, different approximations exist: a direct, yet costly, inversion of radiative transfer models (RTMs); the statistical inversion with in situ data that often results in problems with extrapolation outside the study area; and the most widely adopted hybrid modeling by which statistical models, mostly nonlinear and non-parametric machine learning algorithms, are applied to invert RTM simulations.

no code implementations • 15 Apr 2021 • Devis Tuia, Michele Volpi, Maxime Trolliet, Gustau Camps-Valls

We introduce a method for manifold alignment of different modalities (or domains) of remote sensing images.

1 code implementation • 11 Apr 2021 • Devis Tuia, Ribana Roscher, Jan Dirk Wegner, Nathan Jacobs, Xiao Xiang Zhu, Gustau Camps-Valls

In the last years we have witnessed the fields of geosciences and remote sensing and artificial intelligence to become closer.

no code implementations • 7 Jan 2021 • Ana B. Ruescas, Martin Hieronymi, Sampsa Koponen, Kari Kallio, Gustau Camps-Valls

The coloured dissolved organic matter (CDOM) concentration is the standard measure of humic substance in natural waters.

no code implementations • 22 Dec 2020 • Fernando Mateo, Jordi Munoz-Mari, Valero Laparra, Jochem Verrelst, Gustau Camps-Valls

Gaussian Processes (GPs) has experienced tremendous success in geoscience in general and for bio-geophysical parameter retrieval in the last years.

no code implementations • 11 Dec 2020 • Alvaro Moreno-Martinez, Gustau Camps-Valls, Jens Kattge, Nathaniel Robinson, Markus Reichstein, Peter van Bodegom, Koen Kramer, J. Hans C. Cornelissen, Peter Reich, Michael Bahn, Ulo Niinemets, Josep Peñuelas, Joseph Craine, Bruno E. L. Cerabolini, Vanessa Minden, Daniel C. Laughlin, Lawren Sack, Brady Allred, Christopher Baraloto, Chaeho Byun, Nadejda A. Soudzilovskaia, Steven W. Running

The processing chain exploits machine learning techniques along with optical remote sensing data (MODIS/Landsat) and climate data for gap filling and up-scaling of in-situ measured leaf traits.

Applications Applied Physics

no code implementations • 11 Dec 2020 • Anna Mateo-Sanchis, Maria Piles, Jordi Muñoz-Marí, Jose E. Adsuara, Adrián Pérez-Suay, Gustau Camps-Valls

Developing accurate models of crop stress, phenology and productivity is of paramount importance, given the increasing need of food.

no code implementations • 11 Dec 2020 • Alvaro Moreno-Martinez, Marco Maneta, Gustau Camps-Valls, Luca Martino, Nathaniel Robinson, Brady Allred, Steven W Running

Products derived from a single multispectral sensor are hampered by a limited spatial, spectral or temporal resolutions.

no code implementations • 11 Dec 2020 • Anna Mateo-Sanchis, Jordi Munoz-Mari, Manuel Campos-Taberner, Javier Garcia-Haro, Gustau Camps-Valls

In this work we evaluate multi-output (MO) Gaussian Process (GP) models based on the linear model of coregionalization (LMC) for estimation of biophysical parameter variables under a gap filling setup.

no code implementations • 9 Dec 2020 • Luis Gómez-Chova, Gonzalo Mateo-García, Jordi Muñoz-Marí, Gustau Camps-Valls

This paper presents the development and implementation of a cloud detection algorithm for Proba-V.

no code implementations • 9 Dec 2020 • Devis Tuia, Benjamin Kellenberger, Adrian Pérez-Suay, Gustau Camps-Valls

With a single model, we are able to outline clouds along all year and during day and night with high accuracy.

no code implementations • 9 Dec 2020 • David Malmgren-Hansen, Valero Laparra, Allan Aasbjerg Nielsen, Gustau Camps-Valls

We compare Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF) for dimensionality reduction, and study the compactness and information content of the extracted features.

no code implementations • 9 Dec 2020 • Gonzalo Mateo-García, Luis Gómez-Chova, Gustau Camps-Valls

Convolutional neural networks (CNN) have proven to be state of the art methods for many image classification tasks and their use is rapidly increasing in remote sensing problems.

no code implementations • 9 Dec 2020 • Anna Mateo-Sanchis, Jordi Muñoz-Marí, Adrián Pérez-Suay, Gustau Camps-Valls

This paper introduces warped Gaussian processes (WGP) regression in remote sensing applications.

no code implementations • 9 Dec 2020 • Emiliano Díaz, Adrián Pérez-Suay, Valero Laparra, Gustau Camps-Valls

This paper introduces a novel statistical regression framework that allows the incorporation of consistency constraints.

1 code implementation • 9 Dec 2020 • Juan Emmanuel Johnson, Valero Laparra, Gustau Camps-Valls

Gaussian Processes (GPs) are a class of kernel methods that have shown to be very useful in geoscience applications.

no code implementations • 9 Dec 2020 • José A. Padrón-Hidalgo, Valero Laparra, Nathan Longbotham, Gustau Camps-Valls

Anomalous change detection (ACD) is an important problem in remote sensing image processing.

no code implementations • 8 Dec 2020 • Adrián Pérez-Suay, Julia Amorós-López, Luis Gómez-Chova, Jordi Muñoz-Marí, Dieter Just, Gustau Camps-Valls

Landmark recognition and matching is a critical step in many Image Navigation and Registration (INR) models for geostationary satellite services, as well as to maintain the geometric quality assessment (GQA) in the instrument data processing chain of Earth observation satellites.

no code implementations • 8 Dec 2020 • José A. Padrón Hidalgo, Adrián Pérez-Suay, Fatih Nar, Gustau Camps-Valls

In this work we propose a method to find anomalous changes in remote sensing images based on the chronochrome approach.

no code implementations • 8 Dec 2020 • Fatih Nar, Adrián Pérez-Suay, José Antonio Padrón, Gustau Camps-Valls

This work tackles the target detection problem through the well-known global RX method.

1 code implementation • 8 Dec 2020 • Ana B. Ruescas, Gonzalo Mateo-Garcia, Gustau Camps-Valls, Martin Hieronymi

Water quality parameters are derived applying several machine learning regression methods on the Case2eXtreme dataset (C2X).

no code implementations • 7 Dec 2020 • Fatih Nar, Erdal Yilmaz, Gustau Camps-Valls

We here introduce an automatic Digital Terrain Model (DTM) extraction method.

no code implementations • 7 Dec 2020 • José A. Padrón Hidalgo, Adrián Pérez-Suay, Fatih Nar, Gustau Camps-Valls

In this letter, we propose two families of techniques to improve the efficiency of the standard kernel Reed-Xiaoli (RX) method for anomaly detection by approximating the kernel function with either {\em data-independent} random Fourier features or {\em data-dependent} basis with the Nystr\"om approach.

no code implementations • 7 Dec 2020 • Jochem Verrelst, Juan Pablo Rivera, Anatoly Gitelson, Jesus Delegido, José Moreno, Gustau Camps-Valls

GPR-BAT allows (1) to identify the most informative bands in relating spectral data to a biophysical variable, and (2) to find the least number of bands that preserve optimized accurate predictions.

no code implementations • 7 Dec 2020 • Gustau Camps-Valls, Luca Martino, Daniel H. Svendsen, Manuel Campos-Taberner, Jordi Muñoz-Marí, Valero Laparra, David Luengo, Francisco Javier García-Haro

However, GPs are typically used for inverse modeling based on concurrent observations and in situ measurements only.

no code implementations • 7 Dec 2020 • Manuel Campos-Taberner, Franciso Javier García-Haro, Álvaro Moreno, María Amparo Gilabert, Sergio Sánchez-Ruiz, Beatriz Martínez, Gustau Camps-Valls

We show the first LAI maps obtained with ground data from a smartphone combined with advanced machine learning.

no code implementations • 7 Dec 2020 • Jorge Vicent, Luis Alonso, Luca Martino, Neus Sabater, Jochem Verrelst, Gustau Camps-Valls

Our results indicate that, when compared to a pseudo-random homogeneous distribution of the LUT nodes, GALGA reduces (1) the LUT size by $\sim$75\% and (2) the maximum interpolation relative errors by 0. 5\% It is concluded that automatic LUT design might benefit from the methodology proposed in GALGA to reduce computation time and interpolation errors.

no code implementations • 7 Dec 2020 • Daniel Heestermans Svendsen, Pablo Morales-Álvarez, Rafael Molina, Gustau Camps-Valls

This paper introduces deep Gaussian processes (DGPs) for geophysical parameter retrieval.

no code implementations • 7 Dec 2020 • Aleksandra Wolanin, Gustau Camps-Valls, Luis Gómez-Chova, Gonzalo Mateo-García, Christiaan van der Tol, Yongguang Zhang, Luis Guanter

Satellite remote sensing has been widely used in the last decades for agricultural applications, {both for assessing vegetation condition and for subsequent yield prediction.}

no code implementations • 7 Dec 2020 • Katja Berger, Jochem Verrelst, Jean-Baptiste Féret, Tobias Hank, Matthias Wocher, Wolfram Mauser, Gustau Camps-Valls

Finally, the heteroscedastic GP model was successfully applied on airborne hyperspectral data for N mapping.

no code implementations • 7 Dec 2020 • Adrián Pérez-Suay, Gustau Camps-Valls

Establishing causal relations between random variables from observational data is perhaps the most important challenge in today's \blue{science}.

no code implementations • 7 Dec 2020 • Adrián Pérez-Suay, Julia Amorós-López, Luis Gómez-Chova, Valero Laparra, Jordi Muñoz-Marí, Gustau Camps-Valls

Dealing with land cover classification of the new image sources has also turned to be a complex problem requiring large amount of memory and processing time.

no code implementations • 7 Dec 2020 • Jorge Vicent, Jochem Verrelst, Juan Pablo Rivera-Caicedo, Neus Sabater, Jordi Muñoz-Marí, Gustau Camps-Valls, José Moreno

Computationally expensive Radiative Transfer Models (RTMs) are widely used} to realistically reproduce the light interaction with the Earth surface and atmosphere.

no code implementations • 7 Dec 2020 • ochem Verrelst, Sara Dethier, Juan Pablo Rivera, Jordi Muñoz-Marí, Gustau Camps-Valls, José Moreno

Kernel-based machine learning regression algorithms (MLRAs) are potentially powerful methods for being implemented into operational biophysical variable retrieval schemes.

no code implementations • 7 Dec 2020 • Jose E. Adsuara, Adrián Pérez-Suay, Jordi Muñoz-Marí, Anna Mateo-Sanchis, Maria Piles, Gustau Camps-Valls

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.

no code implementations • 7 Dec 2020 • Adrián Pérez-Suay, Gustau Camps-Valls

Establishing causal relations between random variables from observational data is perhaps the most important challenge in today's Science.

no code implementations • 7 Dec 2020 • Devis Tuia, Diego Marcos, Gustau Camps-Valls

Remote sensing image classification exploiting multiple sensors is a very challenging problem: data from different modalities are affected by spectral distortions and mis-alignments of all kinds, and this hampers re-using models built for one image to be used successfully in other scenes.

no code implementations • 6 Dec 2020 • Miguel Morata-Dolz, Diego Bueso, Maria Piles, Gustau Camps-Valls

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.

no code implementations • 5 Dec 2020 • Luca Pipia, Jordi Muñoz-Marí, Eatidal Amin, Santiago Belda, Gustau Camps-Valls, Jochem Verrelst

The availability of satellite optical information is often hampered by the natural presence of clouds, which can be problematic for many applications.

2 code implementations • 2 Dec 2020 • J. Emmanuel Johnson, Valero Laparra, Gustau Camps-Valls, Raul Santos-Rodríguez, Jesús Malo

Density destructors are differentiable and invertible transforms that map multivariate PDFs of arbitrary structure (low entropy) into non-structured PDFs (maximum entropy).

no code implementations • 18 Oct 2020 • Gustau Camps-Valls, Daniel H. Svendsen, Jordi Cortés-Andrés, Álvaro Moreno-Martínez, Adrián Pérez-Suay, Jose Adsuara, Irene Martín, Maria Piles, Jordi Muñoz-Marí, Luca Martino

Most problems in Earth sciences aim to do inferences about the system, where accurate predictions are just a tiny part of the whole problem.

3 code implementations • 13 Oct 2020 • J. Emmanuel Johnson, Valero Laparra, Maria Piles, Gustau Camps-Valls

Information theory is an excellent framework for analyzing Earth system data because it allows us to characterize uncertainty and redundancy, and is universally interpretable.

4 code implementations • 8 Oct 2020 • Valero Laparra, J. Emmanuel Johnson, Gustau Camps-Valls, Raul Santos-Rodríguez, Jesus Malo

Information theory is an outstanding framework to measure uncertainty, dependence and relevance in data and systems.

2 code implementations • 29 Jul 2020 • J. Emmanuel Johnson, Valero Laparra, Adrián Pérez-Suay, Miguel D. Mahecha, Gustau Camps-Valls

We note that model function derivatives in kernel machines is proportional to the kernel function derivative.

no code implementations • 2 Jul 2020 • Gustau Camps-Valls, Dino Sejdinovic, Jakob Runge, Markus Reichstein

Earth observation (EO) by airborne and satellite remote sensing and in-situ observations play a fundamental role in monitoring our planet.

1 code implementation • 20 May 2020 • J. Emmanuel Johnson, Valero Laparra, Gustau Camps-Valls

In this letter, we demonstrate how one can account for input noise estimates using a GP model formulation which propagates the error terms using the derivative of the predictive mean function.

no code implementations • 13 Dec 2019 • Daniel Heestermans Svendsen, Luca Martino, Gustau Camps-Valls

Many fields of science and engineering rely on running simulations with complex and computationally expensive models to understand the involved processes in the system of interest.

no code implementations • 11 Nov 2019 • Zhu Li, Adrian Perez-Suay, Gustau Camps-Valls, Dino Sejdinovic

We present a regularization approach to this problem that trades off predictive accuracy of the learned models (with respect to biased labels) for the fairness in terms of statistical parity, i. e. independence of the decisions from the sensitive covariates.

no code implementations • 14 Nov 2017 • Daniel Heestermans Svendsen, Luca Martino, Manuel Campos-Taberner, Francisco Javier García-Haro, Gustau Camps-Valls

Radiative transfer models (RTMs) represent mathematically the physical laws which govern the phenomena in remote sensing applications (forward models).

no code implementations • 16 Oct 2017 • Adrián Pérez-Suay, Valero Laparra, Gonzalo Mateo-García, Jordi Muñoz-Marí, Luis Gómez-Chova, Gustau Camps-Valls

It has been shown that not including sensitive features that bias fairness, such as gender or race, is not enough to mitigate the discrimination when other related features are included.

no code implementations • 2 Oct 2017 • Pablo Morales-Alvarez, Adrian Perez-Suay, Rafael Molina, Gustau Camps-Valls

Current remote sensing image classification problems have to deal with an unprecedented amount of heterogeneous and complex data sources.

no code implementations • 21 Nov 2016 • Luca Martino, Victor Elvira, Gustau Camps-Valls

The key point for the successful application of the Gibbs sampler is the ability to draw efficiently samples from the full-conditional probability density functions.

no code implementations • 2 Nov 2016 • Adrián Pérez-Suay, Gustau Camps-Valls

Convergence bounds of both the measure and the sensitivity map are also provided.

no code implementations • 9 Mar 2016 • Emma Izquierdo-Verdiguier, Valero Laparra, Robert Jenssen, Luis Gómez-Chova, Gustau Camps-Valls

Results show that 1) OKECA returns projections with more expressive power than KECA, 2) the most successful rule for estimating the kernel parameter is based on maximum likelihood, and 3) OKECA is more robust to the selection of the length-scale parameter in kernel density estimation.

no code implementations • 31 Jan 2016 • Valero Laparra, Jesus Malo, Gustau Camps-Valls

DRR identifies the nonlinear features through multivariate regression to ensure the reduction in redundancy between he PCA coefficients, the reduction of the variance of the scores, and the reduction in the reconstruction error.

no code implementations • 31 Jan 2016 • Valero Laparra, Sandra Jiménez, Devis Tuia, Gustau Camps-Valls, Jesús Malo

Moreover, PPA shows a number of interesting analytical properties.

no code implementations • 25 Nov 2015 • Adriana Romero, Carlo Gatta, Gustau Camps-Valls

This paper introduces the use of single layer and deep convolutional networks for remote sensing data analysis.

1 code implementation • 9 Apr 2015 • Devis Tuia, Gustau Camps-Valls

We introduce a kernel method for manifold alignment (KEMA) and domain adaptation that can match an arbitrary number of data sources without needing corresponding pairs, just few labeled examples in all domains.

Cannot find the paper you are looking for? You can
Submit a new open access paper.

Contact us on:
hello@paperswithcode.com
.
Papers With Code is a free resource with all data licensed under CC-BY-SA.