1José María Tárraga, Maria Piles, Gustau Camps-Valls, Learning drivers of climate-induced human migrations with Gaussian processes, Presented at NeurIPS 2020 Workshop on Machine Learning for the Developing World,
2Ioannis Papoutsis, Alkyoni Baglatzi, Souzana Touloumtzi, Markus Reichstein, Nuno Carvalhais, Fabian Gans, Gustau Camps-Valls, Maria Piles, Theofilos Kakantousis, Jim Dowling, Manolis Koubarakis, Dimitris Bilidas, Despina-Athanasia Pantazi, George Stamoulis, Christophe Demange, Leo-Gad Journel, Marco Bianchi, Chiara Gervasi, Alessio Rucci, Ioannis Tsampoulatidis, Eleni Kamateri, Tarek Habib, Alejandro Diaz Bolivar, Zisoula Ntasiou, Anastasios Paschalis, DEEPCUBE: EXPLAINABLE AI PIPELINES FOR BIG COPERNICUS DATA, Proceedings of the 2021 conference on Big Data from Space (BiDS’21)2021pdf
3Ioannis Prapas, Spyros Kondylatos, Ioannis Papoutsis, Gustau Camps-Valls, Michele Ronco, Miguel-Ángel Fernández-Torres, Maria Piles Guillem, Nuno Carvalhais, Deep Learning Methods for Daily Wildfire Danger Forecasting, Accepted to the workshop on Artificial Intelligence for Humanitarian Assistance and Disaster Response at the 35th Conference on Neural Information Processing Systems (NeurIPS 2021),
4Dimitris Sykas, Ioannis Papoutsis, Dimitrios Zografakis, Sen4AgriNet: A Harmonized Multi-Country, Multi-Temporal Benchmark Dataset for Agricultural Earth Observation Machine Learning Applications, IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, pp. 5830-5833,
5José María Tárraga, Michele Ronco, Maria Teresa Miranda Espinosa, Maria Piles, Eva Sevillano Marco, Jordi Muñoz, Gustau Camps-Valls, Climate-Induced Displacement with Explainable Machine Learning Models, AGU American Geophysical Union, New Orleans LA USA & Online, 13-17 December 2021, 20212021
6N. I. Bountos, D. Michail and I. Papoutsis, Learning From Synthetic InSAR With Vision Transformers: The Case of Volcanic Unrest Detection, in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-12, 2022, Art no. 4509712, doi:
7Nikolaos Ioannis Bountos, Ioannis Papoutsis, Dimitrios Michail, Nantheera Anantrasirichai, Self-Supervised Contrastive Learning for Volcanic Unrest Detection, IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022, Art no. 3003905,
8Dimitrios Sykas, Maria Sdraka, Dimitrios Zografakis, Ioannis Papoutsis, A Sentinel-2 Multiyear, Multicountry Benchmark Dataset for Crop Classification and Segmentation With Deep Learning, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 15, p. 3323 - 3339, DOI:
9Ronco, M., Prapas, I., Kondylatos, S., Papoutsis, I., Camps-Valls, G., Fernández-Torres, M.-Á., Piles Guillem, M., and Carvalhais, N.: Explainable deep learning for wildfire danger estimation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11787,, 2022.2022
10M. Fernández-Torres, M. Ronco, V. Benson, C. Requena, M. Mahecha, G. Camps-Valls, Explaining Deep Learning Models for Earth Surface Forecasting, Living Planet Symposium, 23-27 May 2022, Bonn, Germany 20222022
11José María Tárraga et al., Inspecting the link between climate and human displacement with Explainable AI and Causal inference, EGU General Assembly, Geophysical Research Abstracts, Hybrid, 23-27 May 2022 Vol. 24 2022,
12N.I. Bountos, I. Papoutsis, D. Michail, A. Karavias, P. Elias, I. Parcharidis, Hephaestus: A large scale multitask dataset towards InSAR understanding, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) EarthVision, 2022,
13Claire Robin, Christian Requena-Mesa, Vitus Benson, Lazaro Alonso, Jeran Poehls, Nuno Carvalhais, Markus Reichstein, Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMs, Artificial Intelligence for Humanitarian Assistance and Disaster Response Workshop at NeurIPS 2022,
14S. Kondylatos, I. Prapas, M. Ronco, I. Papoutsis, G. Camps-Valls, M. Piles, M. Fernández-Torres, N. Carvalhais, Wildfire Danger Prediction and Understanding With Deep Learning, AGU Geophysical Research Letters,
15M. Ioannidou, A. Koukos, V. Sitokonstantinou, I. Papoutsis, C. Kontoes, Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification, Remote Sens. 2022, 14(22), 5739;
16M. Sdraka, I. Papoutsis, B. Psomas, K. Vlachos, K. Ioannidis, K. Karantzalos, I. Gialampoukidis, S. Vrochidis, Deep Learning for Downscaling Remote Sensing Images: Fusion and super-resolution, IEEE Geoscience and Remote Sensing Magazine, vol. 10, no. 3, pp. 202-255, Sept. 2022,
17I. Papoutsis, N. Bountos, A. Zavras, D. Michail, C. Tryfonopoulos, Benchmarking and scaling of deep learning models for land cover image classification, ISPRS Journal of Photogrammetry and Remote Sensing, Volume 195, 2023, Pages 250-268, ISSN 0924-2716,
18Bountos, N. I., Michail, D., Herekakis, T., Thanasou, A., and Papoutsis, I.: Pluto: A global volcanic activity early warning system powered by large scale self-supervised deep learning on InSAR data, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-5913,
19Spyros Kondylatos, Ioannis Prapas, Ioannis Papoutsis, Gustau Camps-Valls, Wildfire danger forecasting with deep learning under label noise, IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2023,
20Nikolaos Ioannis Bountos, Ioannis Papoutsis, Dimitrios Michail, Is realistic synthetic InSAR data generation feasible on low data regimes?, IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2023,
21Spyros Kondylatos, Ioannis Prapas, Gustau Camps-Valls, Ioannis Papoutsis, Mesogeos: A multi-purpose dataset for data-driven wildfire modeling in the Mediterranean, Accepted at the 37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmarks (oral presentation),
22Ronco, M., Tárraga, J.M., Muñoz, J. et al. Exploring interactions between socioeconomic context and natural hazards on human population displacement. Nature Communications 14, 8004 (2023).
23Bao, S.; Alonso, L.; Wang, S.; Gensheimer, J.; De, R.; Carvalhais, N.: Toward robust parameterizations in ecosystem‐level photosynthesis models. Journal of Advances in Modeling Earth Systems 15 (8), e2022MS003464 (2023),
24Son, R.; Stacke, T.; Gayler, V.; Nabel, J. E. M. S.; Schnur, R.; Silva, L. A.; Requena Mesa, C.; Winkler, A.; Hantson, S.; Zaehle, S. and N. Carvalhais: Integration of a deep-learning-based fire model into a global land surface model. Journal of Advances in Modeling Earth Systems 16 (1), e2023MS003710 (2024),
25Michele Ronco, Gustau Camps-Valls, Role of locality, fidelity and symmetry regularization in learning explainable representations, Neurocomputing, Volume 562, 2023, 126884, ISSN 0925-2312,