29 citations · 40 across the 7 of their papers we have counts for
9 papers
Physics-Aware Self-Supervised Pretraining on Multi-Domain Raman Spectra
Charalampos Davalas, Georgios Levantinos, Anastasios Dimitriou +3
Raman spectroscopy generates large volumes of unlabeled data that are difficult to exploit with conventional supervised learning. This work introduces a multi-domain self-supervise…
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…
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…
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…
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…
TEACHING -- Trustworthy autonomous cyber-physical applications through human-centred intelligence
Davide Bacciu, Siranush Akarmazyan, Eric Armengaud +32
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 c…
JGraphT -- A Java library for graph data structures and algorithms
Dimitrios Michail, Joris Kinable, Barak Naveh +1
Mathematical software and graph-theoretical algorithmic packages to efficiently model, analyze and query graphs are crucial in an era where large-scale spatial, societal and econom…