Zobrazeno 1 - 10
of 356
pro vyhledávání: '"Papoutsis, Ioannis"'
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:
Michail, Dimitrios, Panagiotou, Lefki-Ioanna, Davalas, Charalampos, Prapas, Ioannis, Kondylatos, Spyros, Bountos, Nikolaos Ioannis, Papoutsis, Ioannis
With climate change expected to exacerbate fire weather conditions, the accurate anticipation of wildfires on a global scale becomes increasingly crucial for disaster mitigation. In this study, we utilize SeasFire, a comprehensive global wildfire dat
Externí odkaz:
http://arxiv.org/abs/2404.06437
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
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
Deep Learning (DL) is undergoing a paradigm shift with the emergence of foundation models, aptly named by their crucial, yet incomplete nature. In this work, we focus on Contrastive Language-Image Pre-training (CLIP), an open-vocabulary foundation mo
Externí odkaz:
http://arxiv.org/abs/2402.09816
Autor:
Karasante, Ilektra, Alonso, Lazaro, Prapas, Ioannis, Ahuja, Akanksha, Carvalhais, Nuno, Papoutsis, Ioannis
The global occurrence, scale, and frequency of wildfires pose significant threats to ecosystem services and human livelihoods. To effectively quantify and attribute the antecedent conditions for wildfires, a thorough understanding of Earth system dyn
Externí odkaz:
http://arxiv.org/abs/2312.07199
Autor:
Bountos, Nikolaos Ioannis, Sdraka, Maria, Zavras, Angelos, Karasante, Ilektra, Karavias, Andreas, Herekakis, Themistocles, Thanasou, Angeliki, Michail, Dimitrios, Papoutsis, Ioannis
Global floods, exacerbated by climate change, pose severe threats to human life, infrastructure, and the environment. Recent catastrophic events in Pakistan and New Zealand underscore the urgent need for precise flood mapping to guide restoration eff
Externí odkaz:
http://arxiv.org/abs/2311.12056
Autor:
Sdraka, Maria, Dimakos, Alkinoos, Malounis, Alexandros, Ntasiou, Zisoula, Karantzalos, Konstantinos, Michail, Dimitrios, Papoutsis, Ioannis
Over the last decade there has been an increasing frequency and intensity of wildfires across the globe, posing significant threats to human and animal lives, ecosystems, and socio-economic stability. Therefore urgent action is required to mitigate t
Externí odkaz:
http://arxiv.org/abs/2311.03339
Autor:
Prapas, Ioannis, Bountos, Nikolaos Ioannis, Kondylatos, Spyros, Michail, Dimitrios, Camps-Valls, Gustau, Papoutsis, Ioannis
Wildfires are increasingly exacerbated as a result of climate change, necessitating advanced proactive measures for effective mitigation. It is important to forecast wildfires weeks and months in advance to plan forest fuel management, resource procu
Externí odkaz:
http://arxiv.org/abs/2306.10940
We introduce Mesogeos, a large-scale multi-purpose dataset for wildfire modeling in the Mediterranean. Mesogeos integrates variables representing wildfire drivers (meteorology, vegetation, human activity) and historical records of wildfire ignitions
Externí odkaz:
http://arxiv.org/abs/2306.05144