51 citations · 115 across the 12 of their papers we have counts for
12 papers
TIRAuxCloud: A Thermal Infrared Dataset for Day and Night Cloud Detection
Alexis Apostolakis, Vasileios Botsos, Niklas Wölki +4
Clouds are a major obstacle in Earth observation, limiting the usability and reliability of critical remote sensing applications such as fire disaster response, urban heat island m…
FLOGA: A machine learning ready dataset, a benchmark and a novel deep learning model for burnt area mapping with Sentinel-2
Maria Sdraka, Alkinoos Dimakos, Alexandros Malounis +4
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…
Kuro Siwo: 33 billion under the water. A global multi-temporal satellite dataset for rapid flood mapping
Nikolaos Ioannis Bountos, Maria Sdraka, Angelos Zavras +6
Global floods, exacerbated by climate change, pose severe threats to human life, infrastructure, and the environment. Recent catastrophic events in Pakistan and New Zealand undersc…
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…
A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learning
Dimitrios Sykas, Maria Sdraka, Dimitrios Zografakis +1
In this work we introduce Sen4AgriNet, a Sentinel-2 based time series multi country benchmark dataset, tailored for agricultural monitoring applications with Machine and Deep Learn…
Hephaestus: A large scale multitask dataset towards InSAR understanding
Nikolaos Ioannis Bountos, Ioannis Papoutsis, Dimitrios Michail +3
Synthetic Aperture Radar (SAR) data and Interferometric SAR (InSAR) products in particular, are one of the largest sources of Earth Observation data. InSAR provides unique informat…
Self-supervised Contrastive Learning for Volcanic Unrest Detection
Nikolaos Ioannis Bountos, Ioannis Papoutsis, Dimitrios Michail +1
Ground deformation measured from Interferometric Synthetic Aperture Radar (InSAR) data is considered a sign of volcanic unrest, statistically linked to a volcanic eruption. Recent…
Learning from Synthetic InSAR with Vision Transformers: The case of volcanic unrest detection
Nikolaos Ioannis Bountos, Dimitrios Michail, Ioannis Papoutsis
The detection of early signs of volcanic unrest preceding an eruption, in the form of ground deformation in Interferometric Synthetic Aperture Radar (InSAR) data is critical for as…
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…
Benchmarking and scaling of deep learning models for land cover image classification
Ioannis Papoutsis, Nikolaos-Ioannis Bountos, Angelos Zavras +2
The availability of the sheer volume of Copernicus Sentinel-2 imagery has created new opportunities for exploiting deep learning (DL) methods for land use land cover (LULC) image c…