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2Recurrent Neural Networks for Modelling Gross Primary Production
D. Montero, M. Mahecha, F. Martinuzzi, C. Aybar, A. Klosterhalfen, A. Knohl, F. Koebsch, J. Anaya, and S. Wieneke. (2024)
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2Arctic climate response to European radiative forcing: a deep learning study on circulation pattern changes
S. Mehrdad, D. Handorf, I. Höschel, K. Karami, J. Quaas, S. Dipu, and C. Jacobi. Weather and Climate Dynamics, 5 (4): 1223-1268 (2024)
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topic_earthenvironment
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2Earth System Data Cubes: Avenues for advancing Earth system research
D. Montero, G. Kraemer, A. Anghelea, C. Aybar, G. Brandt, G. Camps-Valls, F. Cremer, I. Flik, F. Gans, S. Habershon and 1 other author(s). Environmental Data Science, (2024)
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2Visible neural networks for multi-omics integration: a critical review
D. Selby, R. Jakhmola, M. Sprang, G. Grossmann, H. Raki, N. Maani, D. Pavliuk, J. Ewald, and S. Vollmer. bioRxiv, (2024)
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2Zero-Shot, Big-Shot, Active-Shot - How to estimate cell confluence, lazily
M. Joas, D. Freund, R. Haase, E. Rahm, and J. Ewald. bioRxiv, (2025)
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2KEENsight: Cloud based Collaborative Environment for Streamlining Machine Learning Development
L. Sherpa, S. Attar Khorasani, R. Sankar, R. Müller-Pfefferkorn, and S. Ghiasvand. The Fifteenth International Conference on Information, Intelligence, Systems and Applications, Crete, Greece, (June 2024)
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2Assessing Anonymized System Logs Usefulness for Behavioral Analysis in RNN Models
T. Vargis, and S. Ghiasvand. Proceedings of the International Workshop on Data-driven Resilience Research, 3376, Leipzig, Germany, arXiv, (December 2022)arXiv:2212.01101 cs.
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2Content-Aware Depth-Adaptive Image Restoration
T. Vargis, and S. Ghiasvand. Proceedings of the 29th International Conference on Automation and Computing, Sunderland, UK, (January 2024)arXiv:2401.05049 cs.
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2Unveiling water allocation dynamics: a text analysis of 25 years of stakeholder meetings
T. Carvalho, F. de Assis de Souza Filho, and M. de Brito. Environmental Research Letters, 19 (4): 044066 (2024)
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3A global database of natural hazards impacts reported in the scientific literature
T. Carvalho, J. Zscheischler, C. Kuhlicke, and M. de Brito. EGU General Assembly Conference Abstracts, (2024)
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2Emulation of Forward Modeled Top-of-Atmosphere MODIS-Based Spectral Channels Using Machine Learning
J. Gonzalez, S. Dipu, O. Sourdeval, A. Siméon, G. Camps-Valls, and J. Quaas. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, (2025)
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2Validating Deep Learning Weather Forecast Models on Recent High-Impact Extreme Events
O. Pasche, J. Wider, Z. Zhang, J. Zscheischler, and S. Engelke. Artificial Intelligence for the Earth Systems, 4 (1): e240033 (2025)
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2Arctic Climate Response to European Radiative Forcing: A Deep Learning Approach
S. Mehrdad, D. Handorf, I. Höschel, K. Karami, J. Quaas, S. Dipu, and C. Jacobi. EGUsphere, (2024)
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2Leveraging surface observations and passive satellite retrievals of cloud properties: Applications to cloud type classification and cloud base height retrieval
J. Lenhardt, J. Quaas, D. Sejdinovic, and D. Klocke. (2024)
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2CloudViT: classifying cloud types in global satellite data and in kilometre-resolution simulations using vision transformers
J. Lenhardt, J. Quaas, D. Sejdinovic, and D. Klocke. EGUsphere, (2024)
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2ALAS: Active Learning for Autoconversion Rates Prediction from Satellite Data
M. Novitasari, J. Quaas, and M. Rodrigues. (2024)
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2A Physics-informed Deep Learning Based Clustering to Investigate the Impact of Regional European Radiative Forcing on Arctic Climate and Upper Atmospheric Dynamics
S. Mehrdad, D. Handorf, I. Höschel, K. Karami, J. Quaas, S. Dipu, and C. Jacobi. (2024)
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2Macrophenological dynamics from citizen science plant occurrence data
K. Mora, M. Rzanny, J. Wäldchen, H. Feilhauer, T. Kattenborn, G. Kraemer, P. Mäder, D. Svidzinska, S. Wolf, and M. Mahecha. Methods in Ecology and Evolution, 15 (8): 1422–1437 (July 2024)
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2How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences
S. Jiang, L. Sweet, G. Blougouras, A. Brenning, W. Li, M. Reichstein, J. Denzler, W. Shangguan, G. Yu, F. Huang and 1 other author(s). Earth’s Future, (July 2024)
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1DeepExtremeCubes: Earth system spatio-temporal data for assessing compound heatwave and drought impacts
C. Ji, T. Fincke, V. Benson, G. Camps-Valls, M. Fernández-Torres, F. Gans, G. Kraemer, F. Martinuzzi, D. Montero, K. Mora and 5 other author(s). Scientific Data, 12 (1): 149 (Jan 25, 2025)
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publications (hide)302
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2Recurrent Neural Networks for Modelling Gross Primary Production

2Arctic climate response to European radiative forcing: a deep learning study on circulation pattern changes

2Earth System Data Cubes: Avenues for advancing Earth system research

2Visible neural networks for multi-omics integration: a critical review

2Zero-Shot, Big-Shot, Active-Shot - How to estimate cell confluence, lazily

2KEENsight: Cloud based Collaborative Environment for Streamlining Machine Learning Development

2Assessing Anonymized System Logs Usefulness for Behavioral Analysis in RNN Models

2Content-Aware Depth-Adaptive Image Restoration

2Unveiling water allocation dynamics: a text analysis of 25 years of stakeholder meetings

3A global database of natural hazards impacts reported in the scientific literature

2Emulation of Forward Modeled Top-of-Atmosphere MODIS-Based Spectral Channels Using Machine Learning

2Validating Deep Learning Weather Forecast Models on Recent High-Impact Extreme Events

2Arctic Climate Response to European Radiative Forcing: A Deep Learning Approach

2Leveraging surface observations and passive satellite retrievals of cloud properties: Applications to cloud type classification and cloud base height retrieval

2CloudViT: classifying cloud types in global satellite data and in kilometre-resolution simulations using vision transformers

2ALAS: Active Learning for Autoconversion Rates Prediction from Satellite Data

2A Physics-informed Deep Learning Based Clustering to Investigate the Impact of Regional European Radiative Forcing on Arctic Climate and Upper Atmospheric Dynamics

2Macrophenological dynamics from citizen science plant occurrence data

2How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences

1DeepExtremeCubes: Earth system spatio-temporal data for assessing compound heatwave and drought impacts

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