collaborators

17 papers

stat.ML2026

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…

cs.LG2026

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…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2025

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…