Seasonal Fire Prediction using Spatio-Temporal Deep Neural Networks

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.

Time Series

Deep Learning for Global Wildfire Forecasting

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.

Image Segmentation Semantic Segmentation

TEACHING -- Trustworthy autonomous cyber-physical applications through human-centred intelligence

no code implementations • 14 Jul 2021 • Davide Bacciu, Siranush Akarmazyan, Eric Armengaud, Manlio Bacco, George Bravos, Calogero Calandra, Emanuele Carlini, Antonio Carta, Pietro Cassara, Massimo Coppola, Charalampos Davalas, Patrizio Dazzi, Maria Carmela Degennaro, Daniele Di Sarli, Jürgen Dobaj, Claudio Gallicchio, Sylvain Girbal, Alberto Gotta, Riccardo Groppo, Vincenzo Lomonaco, Georg Macher, Daniele Mazzei, Gabriele Mencagli, Dimitrios Michail, Alessio Micheli, Roberta Peroglio, Salvatore Petroni, Rosaria Potenza, Farank Pourdanesh, Christos Sardianos, Konstantinos Tserpes, Fulvio Tagliabò, Jakob Valtl, Iraklis Varlamis, Omar Veledar

This paper discusses the perspective of the H2020 TEACHING project on the next generation of autonomous applications running in a distributed and highly heterogeneous environment comprising both virtual and physical resources spanning the edge-cloud continuum.

Federated Learning