67 citations · 152 across the 31 of their papers we have counts for
34 papers
MajorTOM:Elliot Pretrain, A Multi-Sensor, Multi-Temporal Pretraining Dataset for Earth Observation
Carmen Oliver Huidobro, Mikolaj Czerkawski, Kevin Monsalvez-Pozo +3
Pretraining generalist Earth observation foundation models requires data that combine several sensors, varied temporal sampling and, increasingly, text, yet open datasets rarely pr…
Climate Variability Modulates the Impact of Price Spikes on Food Insecurity
Jordi Cerdà-Bautista, Vasileios Sitokonstantinou, Homer Durand +3
Climate variability influences whether a market disruption escalates into a food crisis, yet broad climate patterns like El Niño, tracked months before they alter hydro-climatic co…
Credit Access is Associated with Improved Food Security in the Horn of Africa
Jordi Cerdà-Bautista, Vasileios Sitokonstantinou, José Manuel Veiga López-Peña +3
The intensification of climate change poses a growing threat to food security, especially in vulnerable communities. This study employs an observational machine-learning framework…
Optimization Geometry of Equivalent Brownian RKHS Representations
Mahdi Mohammadigohari, Gustau Camps-Valls
Equivalent finite parameterizations can represent the same functions and intrinsic norm yet induce different optimization algorithms. We study this effect in a controlled finite Br…
Disentangling Forced and Internal Climate Variability in Single Realizations using Dynamic Mode Decomposition with Control
Nathan Mankovich, Andrei Gavrilov, Gustau Camps-Valls
We show that a single climate realization can be decomposed into forced and internal components by treating external forcing as a dynamical driver within a linear stochastic system…
Foundation Models in Remote Sensing: Evolving from Unimodality to Multimodality
Danfeng Hong, Chenyu Li, Xuyang Li +2
Remote sensing (RS) techniques are increasingly crucial for deepening our understanding of the planet. As the volume and diversity of RS data continue to grow exponentially, there…
TeleViT1.0: Teleconnection-aware Vision Transformers for Subseasonal to Seasonal Wildfire Pattern Forecasts
Ioannis Prapas, Nikolaos Papadopoulos, Nikolaos-Ioannis Bountos +3
Forecasting wildfires weeks to months in advance is difficult, yet crucial for planning fuel treatments and allocating resources. While short-term predictions typically rely on loc…
Physically consistent and uncertainty-aware learning of spatiotemporal dynamics
Qingsong Xu, Jonathan L Bamber, Nils Thuerey +5
Accurate long-term forecasting of spatiotemporal dynamics remains a fundamental challenge across scientific and engineering domains. Existing machine learning methods often neglect…
Feature Extraction in the Remote Sensing Data Value Chain: A Systematic Review of Methods and Applications
Nathan Mankovich, Kai-Hendrik Cohrs, Homer Durand +3
Earth observation involves collecting, analyzing, and processing an ever-growing mass of data. This planetary data is crucial for addressing relevant societal, economic, and enviro…
Staged Event Trees for Transparent Treatment Effect Estimation
Gherardo Varando, Manuele Leonelli, Jordi Cerdà-Bautista +2
Average and conditional treatment effects are fundamental causal quantities used to evaluate the effectiveness of treatments in various critical applications, including clinical se…
Uncertainty-Aware Deep Learning for Wildfire Danger Forecasting
Spyros Kondylatos, Nikolas Papadopoulos, Gustau Camps-Valls +1
Wildfires are among the most severe natural hazards, posing a significant threat to both humans and natural ecosystems. The growing risk of wildfires increases the demand for forec…
Video Compression for Spatiotemporal Earth System Data
Oscar J. Pellicer-Valero, Cesar Aybar, Gustau Camps Valls
Large-scale Earth system datasets, from high-resolution remote sensing imagery to spatiotemporal climate model outputs, exhibit characteristics analogous to those of standard video…
Geospatial Foundation Models to Enable Progress on Sustainable Development Goals
Pedram Ghamisi, Weikang Yu, Xiaokang Zhang +5
Foundation Models (FMs) are large-scale, pre-trained artificial intelligence (AI) systems that have revolutionized natural language processing and computer vision, and are now adva…
A Collaborative Platform for Soil Organic Carbon Inference Based on Spatiotemporal Remote Sensing Data
Jose Manuel Aroca-Fernandez, Jose Francisco Diez-Pastor, Pedro Latorre-Carmona +4
Soil organic carbon (SOC) is a key indicator of soil health, fertility, and carbon sequestration, making it essential for sustainable land management and climate change mitigation.…
Probabilistic Machine Learning for Noisy Labels in Earth Observation
Spyros Kondylatos, Nikolaos Ioannis Bountos, Ioannis Prapas +3
Label noise poses a significant challenge in Earth Observation (EO), often degrading the performance and reliability of supervised Machine Learning (ML) models. Yet, given the crit…
Learning Causal Response Representations through Direct Effect Analysis
Homer Durand, Gherardo Varando, Gustau Camps-Valls
We propose a novel approach for learning causal response representations. Our method aims to extract directions in which a multidimensional outcome is most directly caused by a tre…
On the Generalization of Representation Uncertainty in Earth Observation
Spyros Kondylatos, Nikolaos Ioannis Bountos, Dimitrios Michail +3
Recent advances in Computer Vision have introduced the concept of pretrained representation uncertainty, enabling zero-shot uncertainty estimation. This holds significant potential…
UrbanSAM: Learning Invariance-Inspired Adapters for Segment Anything Models in Urban Construction
Chenyu Li, Danfeng Hong, Bing Zhang +4
Object extraction and segmentation from remote sensing (RS) images is a critical yet challenging task in urban environment monitoring. Urban morphology is inherently complex, with…
A Flag Decomposition for Hierarchical Datasets
Nathan Mankovich, Ignacio Santamaria, Gustau Camps-Valls +1
Flag manifolds encode nested sequences of subspaces and serve as powerful structures for various computer vision and machine learning applications. Despite their utility in tasks s…
Explainable Earth Surface Forecasting under Extreme Events
Oscar J. Pellicer-Valero, Miguel-Ángel Fernández-Torres, Chaonan Ji +2
With climate change-related extreme events on the rise, high dimensional Earth observation data presents a unique opportunity for forecasting and understanding impacts on ecosystem…
Causal machine learning for sustainable agroecosystems
Vasileios Sitokonstantinou, Emiliano Díaz Salas Porras, Jordi Cerdà Bautista +8
In a changing climate, sustainable agriculture is essential for food security and environmental health. However, it is challenging to understand the complex interactions among its…
Earth System Data Cubes: Avenues for advancing Earth system research
David Montero, Guido Kraemer, Anca Anghelea +15
Recent advancements in Earth system science have been marked by the exponential increase in the availability of diverse, multivariate datasets characterised by moderate to high spa…
AI for Extreme Event Modeling and Understanding: Methodologies and Challenges
Gustau Camps-Valls, Miguel-Ángel Fernández-Torres, Kai-Hendrik Cohrs +22
In recent years, artificial intelligence (AI) has deeply impacted various fields, including Earth system sciences. Here, AI improved weather forecasting, model emulation, parameter…
DeepExtremeCubes: Integrating Earth system spatio-temporal data for impact assessment of climate extremes
Chaonan Ji, Tonio Fincke, Vitus Benson +12
With climate extremes' rising frequency and intensity, robust analytical tools are crucial to predict their impacts on terrestrial ecosystems. Machine learning techniques show prom…