Zobrazeno 1 - 10
of 13 232
pro vyhledávání: '"Camps-Valls"'
Autor:
Pellicer-Valero, Oscar J., Fernández-Torres, Miguel-Ángel, Ji, Chaonan, Mahecha, Miguel D., Camps-Valls, Gustau
With climate change-related extreme events on the rise, high dimensional Earth observation data presents a unique opportunity for forecasting and understanding impacts on ecosystems. This is, however, impeded by the complexity of processing, visualiz
Externí odkaz:
http://arxiv.org/abs/2410.01770
Autor:
Sitokonstantinou, Vasileios, Porras, Emiliano Díaz Salas, Bautista, Jordi Cerdà, Piles, Maria, Athanasiadis, Ioannis, Kerner, Hannah, Martini, Giulia, Sweet, Lily-belle, Tsoumas, Ilias, Zscheischler, Jakob, Camps-Valls, Gustau
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 biophysical, social, and economic components. Predictive machine learn
Externí odkaz:
http://arxiv.org/abs/2408.13155
Autor:
Montero, David, Kraemer, Guido, Anghelea, Anca, Aybar, César, Brandt, Gunnar, Camps-Valls, Gustau, Cremer, Felix, Flik, Ida, Gans, Fabian, Habershon, Sarah, Ji, Chaonan, Kattenborn, Teja, Martínez-Ferrer, Laura, Martinuzzi, Francesco, Reinhardt, Martin, Söchting, Maximilian, Teber, Khalil, Mahecha, Miguel D.
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 spatio-temporal resolutions. Earth System Data Cubes (ESDCs) have emerged
Externí odkaz:
http://arxiv.org/abs/2408.02348
Autor:
Camps-Valls, Gustau, Fernández-Torres, Miguel-Ángel, Cohrs, Kai-Hendrik, Höhl, Adrian, Castelletti, Andrea, Pacal, Aytac, Robin, Claire, Martinuzzi, Francesco, Papoutsis, Ioannis, Prapas, Ioannis, Pérez-Aracil, Jorge, Weigel, Katja, Gonzalez-Calabuig, Maria, Reichstein, Markus, Rabel, Martin, Giuliani, Matteo, Mahecha, Miguel, Popescu, Oana-Iuliana, Pellicer-Valero, Oscar J., Ouala, Said, Salcedo-Sanz, Sancho, Sippel, Sebastian, Kondylatos, Spyros, Happé, Tamara, Williams, Tristan
In recent years, artificial intelligence (AI) has deeply impacted various fields, including Earth system sciences. Here, AI improved weather forecasting, model emulation, parameter estimation, and the prediction of extreme events. However, the latter
Externí odkaz:
http://arxiv.org/abs/2406.20080
Autor:
Ji, Chaonan, Fincke, Tonio, Benson, Vitus, Camps-Valls, Gustau, Fernandez-Torres, Miguel-Angel, Gans, Fabian, Kraemer, Guido, Martinuzzi, Francesco, Montero, David, Mora, Karin, Pellicer-Valero, Oscar J., Robin, Claire, Soechting, Maximilian, Weynants, Melanie, Mahecha, Miguel D.
With climate extremes' rising frequency and intensity, robust analytical tools are crucial to predict their impacts on terrestrial ecosystems. Machine learning techniques show promise but require well-structured, high-quality, and curated analysis-re
Externí odkaz:
http://arxiv.org/abs/2406.18179
Autor:
Cohrs, Kai-Hendrik, Varando, Gherardo, Diaz, Emiliano, Sitokonstantinou, Vasileios, Camps-Valls, Gustau
Causality is essential for understanding complex systems, such as the economy, the brain, and the climate. Constructing causal graphs often relies on either data-driven or expert-driven approaches, both fraught with challenges. The former methods, li
Externí odkaz:
http://arxiv.org/abs/2406.07378
Autor:
Xiong, Zhitong, Wang, Yi, Zhang, Fahong, Stewart, Adam J., Hanna, Joëlle, Borth, Damian, Papoutsis, Ioannis, Saux, Bertrand Le, Camps-Valls, Gustau, Zhu, Xiao Xiang
The development of foundation models has revolutionized our ability to interpret the Earth's surface using satellite observational data. Traditional models have been siloed, tailored to specific sensors or data types like optical, radar, and hyperspe
Externí odkaz:
http://arxiv.org/abs/2403.15356
Detecting latent confounders from proxy variables is an essential problem in causal effect estimation. Previous approaches are limited to low-dimensional proxies, sorted proxies, and binary treatments. We remove these assumptions and present a novel
Externí odkaz:
http://arxiv.org/abs/2403.14228
Autor:
Zhao, Shan, Prapas, Ioannis, Karasante, Ilektra, Xiong, Zhitong, Papoutsis, Ioannis, Camps-Valls, Gustau, Zhu, Xiao Xiang
Wildfire forecasting is notoriously hard due to the complex interplay of different factors such as weather conditions, vegetation types and human activities. Deep learning models show promise in dealing with this complexity by learning directly from
Externí odkaz:
http://arxiv.org/abs/2403.08414
We introduce a causal regularisation extension to anchor regression (AR) for improved out-of-distribution (OOD) generalisation. We present anchor-compatible losses, aligning with the anchor framework to ensure robustness against distribution shifts.
Externí odkaz:
http://arxiv.org/abs/2403.01865