no code implementations • 9 Apr 2024 • Dimitrios Michail, Lefki-Ioanna Panagiotou, Charalampos Davalas, Ioannis Prapas, Spyros Kondylatos, Nikolaos Ioannis Bountos, Ioannis Papoutsis
With climate change expected to exacerbate fire weather conditions, the accurate anticipation of wildfires on a global scale becomes increasingly crucial for disaster mitigation.
no code implementations • 13 Mar 2024 • Shan Zhao, Ioannis Prapas, Ilektra Karasante, Zhitong Xiong, Ioannis Papoutsis, Gustau Camps-Valls, Xiao Xiang Zhu
In that direction, we propose integrating causality with Graph Neural Networks (GNNs) that explicitly model the causal mechanism among complex variables via graph learning.
1 code implementation • 12 Dec 2023 • Ilektra Karasante, Lazaro Alonso, Ioannis Prapas, Akanksha Ahuja, Nuno Carvalhais, Ioannis Papoutsis
The global occurrence, scale, and frequency of wildfires pose significant threats to ecosystem services and human livelihoods.
1 code implementation • 19 Jun 2023 • Ioannis Prapas, Nikolaos Ioannis Bountos, Spyros Kondylatos, Dimitrios Michail, Gustau Camps-Valls, Ioannis Papoutsis
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.
1 code implementation • NeurIPS 2023 • Spyros Kondylatos, Ioannis Prapas, Gustau Camps-Valls, Ioannis Papoutsis
We introduce Mesogeos, a large-scale multi-purpose dataset for wildfire modeling in the Mediterranean.
no code implementations • 18 Nov 2022 • Vanessa Boehm, Wei Ji Leong, Ragini Bal Mahesh, Ioannis Prapas, Edoardo Nemni, Freddie Kalaitzis, Siddha Ganju, Raul Ramos-Pollan
Since such data might not be available during other events or regions, we aimed to produce a landslide density map using only elevation and SAR data to be useful to decision-makers in rapid response scenarios.
1 code implementation • 17 Nov 2022 • Vanessa Böhm, Wei Ji Leong, Ragini Bal Mahesh, Ioannis Prapas, Edoardo Nemni, Freddie Kalaitzis, Siddha Ganju, Raul Ramos-Pollan
In the case of landslides, rapid assessment involves determining the extent of the area affected and measuring the size and location of individual landslides.
1 code implementation • 5 Nov 2022 • Vanessa Boehm, Wei Ji Leong, Ragini Bal Mahesh, Ioannis Prapas, Edoardo Nemni, Freddie Kalaitzis, Siddha Ganju, Raul Ramos-Pollan
With climate change predicted to increase the likelihood of landslide events, there is a growing need for rapid landslide detection technologies that help inform emergency responses.
no code implementations • 1 Nov 2022 • Ioannis Prapas, Akanksha Ahuja, Spyros Kondylatos, Ilektra Karasante, Eleanna Panagiotou, Lazaro Alonso, Charalampos Davalas, Dimitrios Michail, Nuno Carvalhais, Ioannis Papoutsis
We train a deep learning model, which treats global wildfire forecasting as an image segmentation task and skillfully predicts the presence of burned areas 8, 16, 32 and 64 days ahead of time.
no code implementations • 4 Nov 2021 • Ioannis Prapas, Spyros Kondylatos, Ioannis Papoutsis, Gustau Camps-Valls, Michele Ronco, Miguel-Ángel Fernández-Torres, Maria Piles Guillem, Nuno Carvalhais
Wildfire forecasting is of paramount importance for disaster risk reduction and environmental sustainability.