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Found 79 papers, 17 papers with code
Neural Plasticity-Inspired Foundation Model for Observing the Earth Crossing Modalities
The development of foundation models has revolutionized our ability to interpret the Earth's surface using satellite observational data.
Recovering Latent Confounders from High-dimensional Proxy Variables
Detecting latent confounders from proxy variables is an essential problem in causal effect estimation.
Causal Graph Neural Networks for Wildfire Danger Prediction
In that direction, we propose integrating causality with Graph Neural Networks (GNNs) that explicitly model the causal mechanism among complex variables via graph learning.
Improving generalisation via anchor multivariate analysis
We introduce a causal regularisation extension to anchor regression (AR) for improved out-of-distribution (OOD) generalisation.
Causal hybrid modeling with double machine learning
Hybrid modeling integrates machine learning with scientific knowledge to enhance interpretability, generalization, and adherence to natural laws.
Fun with Flags: Robust Principal Directions via Flag Manifolds
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.
Causality and Explainability for Trustworthy Integrated Pest Management
Pesticides serve as a common tool in agricultural pest control but significantly contribute to the climate crisis.
Assessing the Causal Impact of Humanitarian Aid on Food Security
In the face of climate change-induced droughts, vulnerable regions encounter severe threats to food security, demanding urgent humanitarian assistance.
Graphs in State-Space Models for Granger Causality in Climate Science
Granger causality (GC) is often considered not an actual form of causality.
TeleViT: Teleconnection-driven Transformers Improve Subseasonal to Seasonal Wildfire Forecasting
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.
Mesogeos: A multi-purpose dataset for data-driven wildfire modeling in the Mediterranean
We introduce Mesogeos, a large-scale multi-purpose dataset for wildfire modeling in the Mediterranean.
Discovering Causal Relations and Equations from Data
Discovering equations, laws and principles that are invariant, robust and causal explanations of the world has been fundamental in physical sciences throughout the centuries.
Artificial intelligence to advance Earth observation: a perspective
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.
Understanding cirrus clouds using explainable machine learning
Their dependencies on meteorological and aerosol conditions are among the largest uncertainties in global climate models.
The Kernelized Taylor Diagram
Our proposed kernelized Taylor diagram is capable of visualizing similarities between populations with minimal assumptions of the data distributions.
Unsupervised Anomaly and Change Detection with Multivariate Gaussianization
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.
Inference over radiative transfer models using variational and expectation maximization methods
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.
Graph Embedding via High Dimensional Model Representation for Hyperspectral Images
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.
Deep Learning Methods for Daily Wildfire Danger Forecasting
Wildfire forecasting is of paramount importance for disaster risk reduction and environmental sustainability.
Compressed particle methods for expensive models with application in Astronomy and Remote Sensing
In many inference problems, the evaluation of complex and costly models is often required.
Deep Gaussian Processes for Biogeophysical Parameter Retrieval and Model Inversion
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.
Integrating Domain Knowledge in Data-driven Earth Observation with Process Convolutions
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.
Semisupervised Manifold Alignment of Multimodal Remote Sensing Images
We introduce a method for manifold alignment of different modalities (or domains) of remote sensing images.
Towards a Collective Agenda on AI for Earth Science Data Analysis
In the last years we have witnessed the fields of geosciences and remote sensing and artificial intelligence to become closer.
Retrieval of Coloured Dissolved Organic Matter with Machine Learning Methods
The coloured dissolved organic matter (CDOM) concentration is the standard measure of humic substance in natural waters.
Learning Structures in Earth Observation Data with Gaussian Processes
Gaussian Processes (GPs) has experienced tremendous success in geoscience in general and for bio-geophysical parameter retrieval in the last years.
Synergistic Integration of Optical and Microwave Satellite Data for Crop Yield Estimation
Developing accurate models of crop stress, phenology and productivity is of paramount importance, given the increasing need of food.
Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes
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.
A Methodology to Derive Global Maps of Leaf Traits Using Remote Sensing and Climate Data
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
Interpolation and Gap Filling of Landsat Reflectance Time Series
Products derived from a single multispectral sensor are hampered by a limited spatial, spectral or temporal resolutions.
A deep network approach to multitemporal cloud detection
With a single model, we are able to outline clouds along all year and during day and night with high accuracy.
Cloud detection machine learning algorithms for PROBA-V
This paper presents the development and implementation of a cloud detection algorithm for Proba-V.
Warped Gaussian Processes in Remote Sensing Parameter Estimation and Causal Inference
This paper introduces warped Gaussian processes (WGP) regression in remote sensing applications.
Convolutional Neural Networks for Multispectral Image Cloud Masking
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.
Consistent regression of biophysical parameters with kernel methods
This paper introduces a novel statistical regression framework that allows the incorporation of consistency constraints.
Disentangling Derivatives, Uncertainty and Error in Gaussian Process Models
Gaussian Processes (GPs) are a class of kernel methods that have shown to be very useful in geoscience applications.
Kernel Anomalous Change Detection for Remote Sensing Imagery
Anomalous change detection (ACD) is an important problem in remote sensing image processing.
Spatial noise-aware temperature retrieval from infrared sounder data
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.
Pattern Recognition Scheme for Large-Scale Cloud Detection over Landmarks
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.
Nonlinear Cook distance for Anomalous Change Detection
In this work we propose a method to find anomalous changes in remote sensing images based on the chronochrome approach.
Randomized RX for target detection
This work tackles the target detection problem through the well-known global RX method.
Retrieval of Case 2 Water Quality Parameters with Machine Learning
Water quality parameters are derived applying several machine learning regression methods on the Case2eXtreme dataset (C2X).
Nonlinear Distribution Regression for Remote Sensing Applications
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.
Efficient Nonlinear RX Anomaly Detectors
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.
Retrieval of aboveground crop nitrogen content with a hybrid machine learning method
Finally, the heteroscedastic GP model was successfully applied on airborne hyperspectral data for N mapping.
Spectral band selection for vegetation properties retrieval using Gaussian processes regression
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.
Sparsity-driven Digital Terrain Model Extraction
We here introduce an automatic Digital Terrain Model (DTM) extraction method.
Causal Inference in Geoscience and Remote Sensing from Observational Data
Establishing causal relations between random variables from observational data is perhaps the most important challenge in today's \blue{science}.
Causal Inference in Geosciences with Kernel Sensitivity Maps
Establishing causal relations between random variables from observational data is perhaps the most important challenge in today's Science.
Randomized kernels for large scale Earth observation applications
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.
Physics-Aware Gaussian Processes in Remote Sensing
However, GPs are typically used for inverse modeling based on concurrent observations and in situ measurements only.
Gradient-based Automatic Look-Up Table Generator for Atmospheric Radiative Transfer Models
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.
Mapping Leaf Area Index with a Smartphone and Gaussian Processes
We show the first LAI maps obtained with ground data from a smartphone combined with advanced machine learning.
Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval
Kernel-based machine learning regression algorithms (MLRAs) are potentially powerful methods for being implemented into operational biophysical variable retrieval schemes.
Emulation as an Accurate Alternative to Interpolation in Sampling Radiative Transfer Codes
Computationally expensive Radiative Transfer Models (RTMs) are widely used} to realistically reproduce the light interaction with the Earth surface and atmosphere.
Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 using Machine Learning Methods Trained with Radiative Transfer Simulations
Satellite remote sensing has been widely used in the last decades for agricultural applications, {both for assessing vegetation condition and for subsequent yield prediction.}
Deep Gaussian Processes for geophysical parameter retrieval
This paper introduces deep Gaussian processes (DGPs) for geophysical parameter retrieval.
Multi-temporal and multi-source remote sensing image classification by nonlinear relative normalization
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.
Understanding Climate Impacts on Vegetation with Gaussian Processes in Granger Causality
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.
Fusing Optical and SAR time series for LAI gap filling with multioutput Gaussian processes
The availability of satellite optical information is often hampered by the natural presence of clouds, which can be problematic for many applications.
Information Theory in Density Destructors
Density destructors are differentiable and invertible transforms that map multivariate PDFs of arbitrary structure (low entropy) into non-structured PDFs (maximum entropy).
Living in the Physics and Machine Learning Interplay for Earth Observation
Most problems in Earth sciences aim to do inferences about the system, where accurate predictions are just a tiny part of the whole problem.
Gaussianizing the Earth: Multidimensional Information Measures for Earth Data Analysis
Information theory is an excellent framework for analyzing Earth system data because it allows us to characterize uncertainty and redundancy, and is universally interpretable.
Information Theory Measures via Multidimensional Gaussianization
Information theory is an outstanding framework to measure uncertainty, dependence and relevance in data and systems.
Kernel Methods and their derivatives: Concept and perspectives for the Earth system sciences
We note that model function derivatives in kernel machines is proportional to the kernel function derivative.
A Perspective on Gaussian Processes for Earth Observation
Earth observation (EO) by airborne and satellite remote sensing and in-situ observations play a fundamental role in monitoring our planet.
Accounting for Input Noise in Gaussian Process Parameter Retrieval
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.
Active emulation of computer codes with Gaussian processes -- Application to remote sensing
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.
Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness
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.
Joint Gaussian Processes for Biophysical Parameter Retrieval
Radiative transfer models (RTMs) represent mathematically the physical laws which govern the phenomena in remote sensing applications (forward models).
Fair Kernel Learning
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.
Remote Sensing Image Classification with Large Scale Gaussian Processes
Current remote sensing image classification problems have to deal with an unprecedented amount of heterogeneous and complex data sources.
The Recycling Gibbs Sampler for Efficient Learning
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.
Sensitivity Maps of the Hilbert-Schmidt Independence Criterion
Convergence bounds of both the measure and the sensitivity map are also provided.
Optimized Kernel Entropy Components
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.
Dimensionality Reduction via Regression in Hyperspectral Imagery
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.
Unsupervised Deep Feature Extraction for Remote Sensing Image Classification
This paper introduces the use of single layer and deep convolutional networks for remote sensing data analysis.
Kernel Manifold Alignment
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.
