6 citations · 25 across the 11 of their papers we have counts for
12 papers
Tree species mapping in Denmark: A comparison of spectral-temporal features with geospatial foundation model embeddings
Alkiviadis Koukos, Spyros Kondylatos, Thomas Nord-Larsen +3
We map tree species across Denmark using National Forest Inventory plots and EO data, while evaluating the potential of foundation models for large-scale forest characterization. W…
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…
Wildfire spread forecasting with Deep Learning
Nikolaos Anastasiou, Spyros Kondylatos, Ioannis Papoutsis
Accurate prediction of wildfire spread is crucial for effective risk management, emergency response, and strategic resource allocation. In this study, we present a deep learning (D…
Probabilistic Machine Learning for Noisy Labels in Earth Observation
Spyros Kondylatos, Nikolaos Ioannis Bountos, Ioannis Prapas +3
Label noise poses a significant challenge in Earth Observation (EO), often degrading the performance and reliability of supervised Machine Learning (ML) models. Yet, given the crit…
On the Generalization of Representation Uncertainty in Earth Observation
Spyros Kondylatos, Nikolaos Ioannis Bountos, Dimitrios Michail +3
Recent advances in Computer Vision have introduced the concept of pretrained representation uncertainty, enabling zero-shot uncertainty estimation. This holds significant potential…
FireCastNet: Earth-as-a-Graph for Seasonal Fire Prediction
Dimitrios Michail, Charalampos Davalas, Konstantinos Chafis +5
With climate change intensifying fire weather conditions globally, accurate seasonal wildfire forecasting has become critical for disaster preparedness and ecosystem management. We…
AI for Extreme Event Modeling and Understanding: Methodologies and Challenges
Gustau Camps-Valls, Miguel-Ángel Fernández-Torres, Kai-Hendrik Cohrs +22
In recent years, artificial intelligence (AI) has deeply impacted various fields, including Earth system sciences. Here, AI improved weather forecasting, model emulation, parameter…
Seasonal Fire Prediction using Spatio-Temporal Deep Neural Networks
Dimitrios Michail, Lefki-Ioanna Panagiotou, Charalampos Davalas +4
With climate change expected to exacerbate fire weather conditions, the accurate anticipation of wildfires on a global scale becomes increasingly crucial for disaster mitigation. I…
TeleViT: Teleconnection-driven Transformers Improve Subseasonal to Seasonal Wildfire Forecasting
Ioannis Prapas, Nikolaos Ioannis Bountos, Spyros Kondylatos +3
Wildfires are increasingly exacerbated as a result of climate change, necessitating advanced proactive measures for effective mitigation. It is important to forecast wildfires week…
Mesogeos: A multi-purpose dataset for data-driven wildfire modeling in the Mediterranean
Spyros Kondylatos, Ioannis Prapas, Gustau Camps-Valls +1
We introduce Mesogeos, a large-scale multi-purpose dataset for wildfire modeling in the Mediterranean. Mesogeos integrates variables representing wildfire drivers (meteorology, veg…
Deep Learning for Global Wildfire Forecasting
Ioannis Prapas, Akanksha Ahuja, Spyros Kondylatos +7
Climate change is expected to aggravate wildfire activity through the exacerbation of fire weather. Improving our capabilities to anticipate wildfires on a global scale is of utter…
Deep Learning Methods for Daily Wildfire Danger Forecasting
Ioannis Prapas, Spyros Kondylatos, Ioannis Papoutsis +5
Wildfire forecasting is of paramount importance for disaster risk reduction and environmental sustainability. We approach daily fire danger prediction as a machine learning task, u…