Zobrazeno 1 - 10
of 118
pro vyhledávání: '"Michail Dimitrios"'
Autor:
Michail, Dimitrios, Panagiotou, Lefki-Ioanna, Davalas, Charalampos, Prapas, Ioannis, Kondylatos, Spyros, Bountos, Nikolaos Ioannis, Papoutsis, Ioannis
With climate change expected to exacerbate fire weather conditions, the accurate anticipation of wildfires on a global scale becomes increasingly crucial for disaster mitigation. In this study, we utilize SeasFire, a comprehensive global wildfire dat
Externí odkaz:
http://arxiv.org/abs/2404.06437
Deep Learning (DL) is undergoing a paradigm shift with the emergence of foundation models, aptly named by their crucial, yet incomplete nature. In this work, we focus on Contrastive Language-Image Pre-training (CLIP), an open-vocabulary foundation mo
Externí odkaz:
http://arxiv.org/abs/2402.09816
Autor:
Bountos, Nikolaos Ioannis, Sdraka, Maria, Zavras, Angelos, Karasante, Ilektra, Karavias, Andreas, Herekakis, Themistocles, Thanasou, Angeliki, Michail, Dimitrios, Papoutsis, Ioannis
Global floods, exacerbated by climate change, pose severe threats to human life, infrastructure, and the environment. Recent catastrophic events in Pakistan and New Zealand underscore the urgent need for precise flood mapping to guide restoration eff
Externí odkaz:
http://arxiv.org/abs/2311.12056
Autor:
Sdraka, Maria, Dimakos, Alkinoos, Malounis, Alexandros, Ntasiou, Zisoula, Karantzalos, Konstantinos, Michail, Dimitrios, Papoutsis, Ioannis
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-economic stability. Therefore urgent action is required to mitigate t
Externí odkaz:
http://arxiv.org/abs/2311.03339
Autor:
Prapas, Ioannis, Bountos, Nikolaos Ioannis, Kondylatos, Spyros, Michail, Dimitrios, Camps-Valls, Gustau, Papoutsis, Ioannis
Wildfires are increasingly exacerbated as a result of climate change, necessitating advanced proactive measures for effective mitigation. It is important to forecast wildfires weeks and months in advance to plan forest fuel management, resource procu
Externí odkaz:
http://arxiv.org/abs/2306.10940
Autor:
Prapas, Ioannis, Ahuja, Akanksha, Kondylatos, Spyros, Karasante, Ilektra, Panagiotou, Eleanna, Alonso, Lazaro, Davalas, Charalampos, Michail, Dimitrios, Carvalhais, Nuno, Papoutsis, Ioannis
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 uttermost importance for mitigating their negative effects. In this work, w
Externí odkaz:
http://arxiv.org/abs/2211.00534
Autor:
Bountos, Nikolaos Ioannis, Papoutsis, Ioannis, Michail, Dimitrios, Karavias, Andreas, Elias, Panagiotis, Parcharidis, Isaak
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 information on diverse geophysical processes and geology, and on the geotechni
Externí odkaz:
http://arxiv.org/abs/2204.09435
Autor:
Bountos, Nikolaos Ioannis, Papoutsis, Ioannis, Michail, Dimitrios, Anantrasirichai, Nantheera
Publikováno v:
IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022
Ground deformation measured from Interferometric Synthetic Aperture Radar (InSAR) data is considered a sign of volcanic unrest, statistically linked to a volcanic eruption. Recent studies have shown the potential of using Sentinel-1 InSAR data and su
Externí odkaz:
http://arxiv.org/abs/2202.04030
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 assessing volcanic hazard. In this work we treat this as a binary classi
Externí odkaz:
http://arxiv.org/abs/2201.03016
Autor:
Papoutsis, Ioannis, Bountos, Nikolaos-Ioannis, Zavras, Angelos, Michail, Dimitrios, Tryfonopoulos, Christos
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 classification. However, an extensive set of benchmark experiments is c
Externí odkaz:
http://arxiv.org/abs/2111.09451