Merged refs

Author: skondylatos

content/publication/karavias-2025/cite.bib

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+@article{karavias2025,
+ author = {Karavias, Andreas and Anastasiou, Dimitrios and Svigkas, Nikos and Papanikolaou, Xanthos and De Astis, Gianfilippo and Atzori, Simone and Papoutsis, Ioannis},
+ journal = {ESS Open Archive},
+ title = {Santorini Inflates Again: Geodetic Monitoring and Modeling of the 2024–2025 Volcanic Unrest},
+ url = {https://essopenarchive.org/users/917515/articles/1290136-santorini-inflates-again-geodetic-monitoring-and-modeling-of-the-2024-2025-volcanic-unrest},
+ year = {2025}
+}

content/publication/karavias-2025/index.md

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+---
+title: "Santorini Inflates Again: Geodetic Monitoring and Modeling of the 2024–2025 Volcanic Unrest"
+authors:
+- Andreas Karavias
+- Dimitrios Anastasiou
+- Nikos Svigkas
+- Xanthos Papanikolaou
+- Gianfilippo De Astis
+- Simone Atzori
+- Ioannis Papoutsis
+date: '2025-04-28'
+publication_types:
+- manuscript
+publication: "ESS Open Archive (Research Letter, Preprint)"
+url_pdf: "https://essopenarchive.org/users/917515/articles/1290136-santorini-inflates-again-geodetic-monitoring-and-modeling-of-the-2024-2025-volcanic-unrest"
+featured: true
+abstract: >
+  We analyze and discuss surface deformation patterns related to the Santorini Caldera unrest, spanning from summer 2024 to January 2025. Synthetic Aperture Radar (SAR) interferometry was performed exploiting the Persistent Scatterer Interferometry (PSI) technique. We used both satellite geometries of the Sentinel-1 mission, producing line-of-sight (LOS) deformation maps and displacement decomposition in vertical and horizontal components. The observed displacements were combined with measurements from four GNSS stations across Santorini island. The results highlight horizontal movements and uplift up to 70 mm/yr with a radial inflation pattern centered around the Kameni islands, reminiscent of the 2011-2012 inflation episode. The geophysical model for the 2024-2025 period suggests a positive volume rate change at 2.9 km depth. Compared to the 2011-2012 unrest, the volume rate change is smaller; potentially a mix of magma and gas may be causing pressure along Kameni’s volcano-tectonic line, this time with a shallower source than the previous unrest.
+---

content/publication/papadopoulos-2025-hephaestus/cite.bib

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+@article{papadopoulos2025hephaestus,
+ author = {Papadopoulos, Nikolas and Bountos, Nikolaos Ioannis and Sdraka, Maria and Karavias, Andreas and Papoutsis, Ioannis},
+ journal = {arXiv preprint arXiv:2505.17782},
+ title = {Hephaestus Minicubes: A Global, Multi-Modal Dataset for Volcanic Unrest Monitoring},
+ year = {2025}
+}

content/publication/papadopoulos-2025-hephaestus/index.md

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+---
+title: 'Hephaestus Minicubes: A Global, Multi-Modal Dataset for Volcanic Unrest Monitoring'
+authors:
+- Nikolas Papadopoulos
+- Nikolaos Ioannis Bountos
+- Maria Sdraka
+- Andreas Karavias
+- Ioannis Papoutsis
+date: '2025-04-01'
+publishDate: '2025-09-24T06:06:23.933104Z'
+publication_types:
+- article-journal
+publication: '*arXiv preprint arXiv:2505.17782*'
+abstract: "Ground deformation is regarded in volcanology as a key precursor signal preceding volcanic eruptions. Satellite-based Interferometric Synthetic Aperture Radar (InSAR) enables consistent, global-scale deformation tracking; however, deep learning methods remain largely unexplored in this domain, mainly due to the lack of a curated machine learning dataset. In this work, we build on the existing Hephaestus dataset, and introduce Hephaestus Minicubes, a global collection of 38 spatiotemporal datacubes offering high resolution, multi-source and multi-temporal information, covering 44 of the world's most active volcanoes over a 7-year period. Each spatiotemporal datacube integrates InSAR products, topographic data, as well as atmospheric variables which are known to introduce signal delays that can mimic ground deformation in InSAR imagery. Furthermore, we provide expert annotations detailing the type, intensity and spatial extent of deformation events, along with rich text descriptions of the observed scenes. Finally, we present a comprehensive benchmark, demonstrating Hephaestus Minicubes' ability to support volcanic unrest monitoring as a multi-modal, multi-temporal classification and semantic segmentation task, establishing strong baselines with state-of-the-art architectures. This work aims to advance machine learning research in volcanic monitoring, contributing to the growing integration of data-driven methods within Earth science applications. "
+
+tags:
+- InSAR
+- Earth Observation
+- Volcanoes
+
+categories: ['Code', 'Datasets']
+featured: false
+url_code: 'https://github.com/Orion-AI-Lab/Hephaestus-minicubes'
+url_dataset: ''
+
+links:
+- name: URL
+  url: https://arxiv.org/abs/2505.17782
+---

content/publication/zhao-causal-2024/cite.bib

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+@misc{zhao_causal_2024,
+ abstract = {Wildfire forecasting is notoriously hard due to the complex interplay of different factors such as weather conditions, vegetation types and human activities. Deep learning models show promise in dealing with this complexity by learning directly from data. However, to inform critical decision making, we argue that we need models that are right for the right reasons; that is, the implicit rules learned should be grounded by the underlying processes driving wildfires. In that direction, we propose integrating causality with Graph Neural Networks (GNNs) that explicitly model the causal mechanism among complex variables via graph learning. The causal adjacency matrix considers the synergistic effect among variables and removes the spurious links from highly correlated impacts. Our methodology's effectiveness is demonstrated through superior performance forecasting wildfire patterns in the European boreal and mediterranean biome. The gain is especially prominent in a highly imbalanced dataset, showcasing an enhanced robustness of the model to adapt to regime shifts in functional relationships. Furthermore, SHAP values from our trained model further enhance our understanding of the model's inner workings.},
+ author = {Zhao, Shan and Prapas, Ioannis and Karasante, Ilektra and Xiong, Zhitong and Papoutsis, Ioannis and Camps-Valls, Gustau and Zhu, Xiao Xiang},
+ doi = {10.48550/arXiv.2403.08414},
+ file = {Snapshot:C\:\\Users\ıprap\\Zotero\\storage\\D6YJL6FX\\2403.html:text/html;Zhao et al_2024_Causal Graph Neural Networks for Wildfire Danger Prediction.pdf:C\:\\Users\ıprap\\Zotero\\storage\\7SVTYJ4P\\Zhao et al_2024_Causal Graph Neural Networks for Wildfire Danger Prediction.pdf:application/pdf},
+ month = {March},
+ note = {arXiv:2403.08414 [cs]},
+ publisher = {arXiv},
+ title = {Causal Graph Neural Networks for Wildfire Danger Prediction},
+ url = {http://arxiv.org/abs/2403.08414},
+ urldate = {2025-09-24},
+ year = {2024}
+}

content/publication/zhao-causal-2024/index.md

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+---
+title: Causal Graph Neural Networks for Wildfire Danger Prediction
+authors:
+- Shan Zhao
+- Ioannis Prapas
+- Ilektra Karasante
+- Zhitong Xiong
+- Ioannis Papoutsis
+- Gustau Camps-Valls
+- Xiao Xiang Zhu
+date: '2024-03-01'
+publishDate: '2025-09-24T06:28:41.317257Z'
+publication_types:
+- manuscript
+publication: '*arXiv*'
+doi: 10.48550/arXiv.2403.08414
+abstract: Wildfire forecasting is notoriously hard due to the complex interplay of
+  different factors such as weather conditions, vegetation types and human activities.
+  Deep learning models show promise in dealing with this complexity by learning directly
+  from data. However, to inform critical decision making, we argue that we need models
+  that are right for the right reasons; that is, the implicit rules learned should
+  be grounded by the underlying processes driving wildfires. In that direction, we
+  propose integrating causality with Graph Neural Networks (GNNs) that explicitly
+  model the causal mechanism among complex variables via graph learning. The causal
+  adjacency matrix considers the synergistic effect among variables and removes the
+  spurious links from highly correlated impacts. Our methodology's effectiveness is
+  demonstrated through superior performance forecasting wildfire patterns in the European
+  boreal and mediterranean biome. The gain is especially prominent in a highly imbalanced
+  dataset, showcasing an enhanced robustness of the model to adapt to regime shifts
+  in functional relationships. Furthermore, SHAP values from our trained model further
+  enhance our understanding of the model's inner workings.
+links:
+- name: URL
+  url: http://arxiv.org/abs/2403.08414
+---