PUBLICATIONS
A month-by-month overview of all publications by members of the ELLIS Unit Jena, highlighting papers where they appear as first or last authors, starting from the unit’s founding in 2022.
2026 (15)
February 2026 (4)
G. Camps-Valls (2026). AI needs a new philosophy of science. The Innovation, 101311. https://doi.org/10.1016/j.xinn.2026.101311
D. Han, Y. Li, J. Denzler (2026). Realistic Face Reconstruction from Facial Embeddings via Diffusion Models. arXiv preprint arXiv:2602.13168. https://doi.org/10.48550/arXiv.2602.13168
M. D. Mahecha, G. Kraemer, M. Reinhardt, D. Montero, F. Gans, A. Bastos, H. Feilhauer, I. Flik, C. Ji, T. Kattenborn, M. Migliavacca, M. Mönks, J. Quaas, S. Sippel, S. Walther, S. Wieneke, C. Wirth, G. Camps-Valls (2026). Accelerated north-east shift of the global green wave trajectory. Proceedings of the National Academy of Sciences (PNAS). https://doi.org/10.1073/pnas.2515835123
S. Chen, R. Zheng, J. Huang, S. Jiang (2026). High-frequency monitoring reveals rainfall-driven phosphorus and nitrogen concentration–discharge patterns and associated mechanisms. Journal of Hydrology, 669, 135151. https://doi.org/10.1016/j.jhydrol.2026.135151
January 2026 (11)
Peter Pfleiderer, Anna Merrifield, István Dunkl, Homer Durand, Enora Cariou, Julien Cattiaux, Gustau Camps-Valls, & Sebastian Sippel (2026).
Considerable yet contrasting regional imprint of circulation change on summer temperature trends across the Northern Hemisphere mid-latitudes. Weather and Climate Dynamics, 7, 89. https://doi.org/10.5194/wcd-7-89-2026
Lindenlaub, L., Weigel, K., Hassler, B., Jones, C., & Eyring, V. (2026). Characteristics of agricultural droughts in CMIP6 historical simulations and future projections. Earth System Dynamics, 17 (1), 81-105. https://esd.copernicus.org/articles/17/81/2026/
Schneider, F., & Denzler, J. (2026). FreeStylo: An easy-to-use stylistic device detection tool for stylometry. Journal of Open Source Software, 11 (117), 7596. https://joss.theoj.org/papers/10.21105/joss.07596.pdf
Li, W., Duveiller, G., Gans, F., Smits, J., Kraemer, G., Frank, D., Mahecha, M. D., Weber, U., Migliavacca, M., Ceglar, A., Keenan, T. F., & Reichstein, M. (2026). Globally synchronized changes in the biosphere, atmosphere, and society identified using public data. One Earth, 9 (1). https://www.cell.com/one-earth/fulltext/S2590-3322(25)00385-9
Bourriz, M., Laamrani, A., Abdelali, H. A., Bourzeix, F., El-Battay, A., Amazirh, A., & Chehbouni, A. (2026). How Effective Are Foundation Models for Crop Type Mapping Using Hyperspectral Imaging? A Comparative Study of Machine Learning, Deep Learning, and Geospatial Foundation Models. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48, 69-74. https://isprs-archives.copernicus.org/articles/XLVIII-4-W17-2025/69/2026/
Kraft, B., Stalder, S., Aeberhard, W. H., Ruiz, N. H., Meinshausen, N., Shen, X., & Gudmundsson, L. (2026). Modeling uncertainty with engression: A deep generative time‐series approach. Geophysical Research Letters, 53 (2), e2025GL120122. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2025GL120122
Lamb, K. D., Singer, C. E., Loftus, K., Morrison, H., Powell, M., Ko, J., Buch, J., Hu, A. Z., Walqui, M. v. L., & Gentine, P. (2026). Perspectives on systematic cloud microphysics scheme development with machine learning. Journal of Advances in Modeling Earth Systems, 18 (1), e2025MS005341. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2025MS005341
Fan, H., Bai, L., Fei, B., Xiao, Y., Chen, K., Liu, Y., Qu, Y., Ling, F., & Gentine, P. (2026). Physically consistent global atmospheric data assimilation with machine learning in latent space. Science Advances, 12 (1), eaea4248. https://www.science.org/doi/abs/10.1126/sciadv.aea4248
Ma, C., Pellicer-Valero, O. J., Cohrs, K., Yao, J., Jiang, J., Li, H., & Camps-Valls, G. (2026). Unveiling Spectral Attention Redundancy with Explainable AI. IEEE Geoscience and Remote Sensing Letters. https://ieeexplore.ieee.org/abstract/document/11352849/
Messow, V., Siederdissen, C. H. z., & Habeck, M. (2026). zelll: a fast, framework-free, and flexible implementation of the cell lists algorithm for the rust programming language. Bioinformatics Advances, vbaf330. https://academic.oup.com/bioinformaticsadvances/advance-article-abstract/doi/10.1093/bioadv/vbaf330/8414225
J. Reuss, J. Macdonald, S. Becker, E. Gikalo, K. Schultka, L. Richter, M. Körner (2026). Benchmarking for practice: Few-shot time-series crop-type classification on the EUROCROPSML dataset. ISPRS Open Journal of Photogrammetry and Remote Sensing. https://doi.org/10.1016/j.ophoto.2026.100117
2025 (114)
December 2025 (6)
ElGhawi, R., Reimers, C., Schnur, R., Reichstein, M., Körner, M., Carvalhais, N., & Winkler, A. J. (2025). Hybrid‐modeling of land‐atmosphere fluxes using integrated machine learning in the ICON‐ESM modeling framework. Journal of Advances in Modeling Earth Systems, 17 (12), e2025MS005102. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2025MS005102
ElGhawi, R., Winkler, A. J., Reimers, C., Schall, A., Gensheimer, J., & Kraft, B. (2025). Imitation or identification: limitations of deep learning in extrapolating to future climate-carbon cycle change. Machine Learning: Earth, 1 (1), 01LT02. https://iopscience.iop.org/article/10.1088/3049-4753/ae2279/meta
Asaad, I., Jacquelin, M., Perrotin, O., Girin, L., & Hueber, T. (2025). Is self-supervised learning enough to fill in the gap? A study on speech inpainting. Computer Speech & Language, 101922. https://www.sciencedirect.com/science/article/pii/S0885230825001470
Grundner, A., Beucler, T., Savre, J., Lauer, A., Schlund, M., & Eyring, V. (2025). Reduced cloud cover errors in a hybrid AI-climate model through equation discovery and automatic tuning. Scientific Reports. https://www.nature.com/articles/s41598-025-29155-3
A. Wijayawardhana, D. Gunawan, T. Suesse (2026). Bayesian inference for non-Gaussian simultaneous autoregressive models with missing data. Spatial Statistics, 100966. Elsevier. https://arxiv.org/abs/2505.23070
Habeck, M., Saleem, H. N., Plota, D., Cheruiyot, C., Kohl, T., Lehnart, S. E., Jakobs, S., & Ebert, A. (2025). Sarcomere analysis in human cardiomyocytes by computing radial frequency spectra. Biological chemistry, NA (0). https://www.degruyterbrill.com/document/doi/10.1515/hsz-2025-0173/html
November 2025 (6)
Bourriz, M., Laamrani, A., Abdelali, H. A., Bourzeix, F., Bouanani, N. E., & Chehbouni, A. (2025). Assessing Hyperspectral Sensor Capabilities: PRISMA vs EnMAP for Crop Type Mapping in a Semi-Arid Region. Remote Sensing Applications: Society and Environment, 101815. https://www.sciencedirect.com/science/article/pii/S2352938525003684
Nathaniel, J., Roesch, C., Buch, J., DeSantis, D., Rupe, A., Lamb, K. D., & Gentine, P. (2025). Deep Koopman operators for causal discovery. Communications Physics, 8 (1), 513. https://www.nature.com/articles/s42005-025-02426-1
Teber, K., Weynants, M., Gans, F., & Mahecha, M. (2025). Geo-Disasters: geocoding climate-related events in the international disaster database EM-DAT. Big Earth Data, https://doi.org/10.1080/20964471.2025.25. https://www.tandfonline.com/doi/abs/10.1080/20964471.2025.2576274
Brovkin, V., Sanderson, B. M., Brizuela, N. G., Hajima, T., Ilyina, T., Jones, C. D., Koven, C., Lawrence, D., Lawrence, P., Li, H., Liddcoat, S., Romanou, A., Séférian, R., Sentman, L. T., Swann, A. L., Tjiputra, J., Ziehn, T., & Winkler, A. J. (2025). On a simplified solution of climate-carbon dynamics in idealized flat10MIP simulations. Earth System Dynamics, 16 (6), 2021-2034. https://esd.copernicus.org/articles/16/2021/2025/
Schwabe, M., Pastori, L., Sarandrea, V., & Eyring, V. (2025). Quantum Machine Learning for Climate Modelling. NA, 73-78. https://ieeexplore.ieee.org/abstract/document/11344653/
Lucchese, L. V., Oliveira, G. G. d., Pedrollo, O. C., & Brenning, A. (2025). Spatially distributed antecedent rainfall thresholds for landslide occurrence: a multitask machine learning modelling approach. Hydrological Sciences Journal, 70 (15), 2785-2798. https://www.tandfonline.com/doi/abs/10.1080/02626667.2025.2561163
October 2025 (5)
Braun, T., Vallejo-Bernal, S., Marwan, N., Kurths, J., Quaas, J., Diaz-Guilera, A., Gimeno, L., & Mahecha, M. (2025). Atmospheric river trajectories organise along a global transport network. https://www.researchsquare.com/article/rs-7482510/v1. https://www.researchsquare.com/article/rs-7482510/latest
Chen, S., Blougouras, G., Calamita, E., Lee, S., Fatecha, B., Zheng, R., Huang, J., & Jiang, S. (2025). Diverse environmental factors shape patterns of terrestrial and riverine productivity decoupling. Geophysical Research Letters, 52 (20), e2025GL118748. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2025GL118748
Vemuri, S. K., Valapil, A. A. C., Büchner, T., & Denzler, J. (2025). RamPINN: Recovering Raman Spectra From Coherent Anti-Stokes Spectra Using Embedded Physics. arXiv e-prints, arXiv: 2510.06020. https://ui.adsabs.harvard.edu/abs/2025arXiv251006020K/abstract
Yu, X., Orth, R., Reichstein, M., Reimers, C., Gomarasca, U., Migliavacca, M., Papale, D., Bahn, M., & Bastos, A. (2025). Widespread but Divergent Drought Legacy Effects on Gross Primary Productivity Across Biomes. Global Change Biology, 31 (10), e70541. https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.70541
Wenig, M., Rump, P. G., Blacher, M., & Giesen, J. (2025). The syntax and semantics of einsum. CoRR, abs/2509.20020. https://arxiv.org/abs/2509.20020
September 2025 (12)
Frank, J. K., Suesse, T., & Brenning, A. (2025). An assessment of spatial random forests for environmental mapping: The case of groundwater nitrate concentration. Environmental Modelling & Software, 193, 106626. https://doi.org/10.1016/j.envsoft.2025.106626
Blunk, J., Bodesheim, P., & Denzler, J. (2025). Adaptive Model Selection for Expanded Post Hoc Debiasing and Mitigating Varying Degrees of Spurious Correlations. NA, 101-111. https://link.springer.com/chapter/10.1007/978-3-032-05060-1_9
Deylen, F. v., Eulzer, P., & Lawonn, K. (2025). AortaAnalyzer: Interactive, integrated CTA aorta segmentation and quantitative analysis platform. Computers & Graphics, 104415. https://www.sciencedirect.com/science/article/pii/S0097849325002560
Valapil, A. A. C., Messerschmidt, C., Shadaydeh, M., Schmitt, M., Popp, J., & Denzler, J. (2025). Deep Learning-Assisted Dynamic Mode Decomposition for Non-resonant Background Removal in CARS Spectroscopy. NA, 41-56. https://link.springer.com/chapter/10.1007/978-3-032-12840-9_4
Merk, N., Sterzik, A., & Lawonn, K. (2025). DeepSES: Learning solvent-excluded surfaces via neural signed distance fields. Computers & Graphics, 104392. https://www.sciencedirect.com/science/article/pii/S009784932500233X
Qing, Y., Wang, S., AghaKouchak, A., & Gentine, P. (2025). Delayed formation of Arctic snow cover in response to wildland fires in a warming climate. Nature Climate Change, 15 (10), 1091-1098. https://www.nature.com/articles/s41558-025-02443-6
Shadaydeh, M., Noering, V., Franz, M., Chand, T., Croy, I., & Denzler, J. (2025). Directionality of Interpersonal Neural Influence in Functional Near‐Infrared Spectroscopy Hyperscanning: Feasibility of Information–Theoretic Causality Analysis in Motor Tasks. European Journal of Neuroscience, 62 (6), e70252. https://onlinelibrary.wiley.com/doi/abs/10.1111/ejn.70252
Yang, Y., & Wolfers, T. (2025). Hierarchical Characterization of Brain Dynamics via State Space-Based Vector Quantization. NA, 394-404. https://link.springer.com/chapter/10.1007/978-3-032-05162-2_38
Hafner, K., Iglesias‐Suarez, F., Shamekh, S., Gentine, P., Giorgetta, M. A., Pincus, R., & Eyring, V. (2025). Interpretable machine learning‐based radiation emulation for icon. Journal of Geophysical Research: Machine Learning and Computation, 2 (4), e2024JH000501. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2024JH000501
Gentine, P. (2025). Launching Machine Learning: Earth—a new forum for machine learning in the Earth sciences. NA, 1 (1), 010201. https://iopscience.iop.org/article/10.1088/3049-4753/adeb8b/meta
Preßler, R., Meuschke, M., Preim, B., & Lawonn, K. (2025). Visualization support for remote collaborative aneurysm Treatment planning. International Journal of Computer Assisted Radiology and Surgery, 1-10. https://link.springer.com/article/10.1007/s11548-025-03508-9
Voigt, H., Kahlmeyer, P., Lawonn, K., Habeck, M., & Giesen, J. (2025). Analyzing generalization in pre-trained symbolic regression. CoRR, abs/2509.19849. https://arxiv.org/abs/2509.19849
August 2025 (12)
Wang, Z., Pasternack, G. B., Jin, Y., Rampini, C., Alexander, S., Kumar, N., Storesund, R., Perales, K. M., Lim, C., Moreno, S., & Lacan, I. (2025). Machine learning-guided field site selection for river classification. International Journal of Applied Earth Observation and Geoinformation, 142, 104742. https://www.sciencedirect.com/science/article/pii/S1569843225003899?via%3Dihub
Sarauer, E., Schwabe, M., Weiss, P., Lauer, A., Stier, P., & Eyring, V. (2025). A physics-informed machine learning parameterization for cloud microphysics in ICON. Environmental Data Science, 4, e40. https://www.cambridge.org/core/journals/environmental-data-science/article/physicsinformed-machine-learning-parameterization-for-cloud-microphysics-in-icon/9EEF4A2B900F09D65475E62A3390C177
Sterzik, A., Lednický, T., Csáki, A., & Lawonn, K. (2025). A visualization framework for localized surface plasmon resonance imaging in sensing applications. Computers & Graphics, 104396. https://www.sciencedirect.com/science/article/pii/S0097849325002377
Hombeck, J., Voigt, H., & Lawonn, K. (2025). Beyond buttons: A user-centric approach to hands-free locomotion in Virtual Reality via voice commands. Computers & Graphics, 104318. https://www.sciencedirect.com/science/article/pii/S0097849325001591
Brehm, G., & Bodesheim, P. (2025). BiodivKI: Erfassung der Biodiversität von Nachtfaltern (Lepidoptera) mit automatisierten Kamerafallen und künstlicher Intelligenz: Perspektiven für ein großflächiges Monitoring …. NA. https://oa.tib.eu/renate/items/43cf35d4-a3af-4a5d-bdb9-31bd70dfa216
Venkataramanan, A., & Denzler, J. (2025). Distance-informed Neural Processes. Advances in Neural Information Processing Systems (NeurIPS) 2025. https://arxiv.org/abs/2508.18903
Kraft, B., Nelson, J. A., Walther, S., Gans, F., Weber, U., Duveiller, G., Reichstein, M., Zhang, W., Rußwurm, M., Tuia, D., Körner, M., Hamdi, Z., & Jung, M. (2025). On the added value of sequential deep learning for the upscaling of evapotranspiration. Biogeosciences, 22 (15), 3965-3987. https://bg.copernicus.org/articles/22/3965/2025/
Lohse, C., & Wahl, J. (2025). Sortability of Time Series Data. Transactions on Machine Learning Research (TMLR). ISSN: 2835-8856., NA (https://openreview.net/forum?id=OGvmCpcH). https://arxiv.org/abs/2407.13313
Williams, T. K., Martínez, Á. M., Martinuzzi, F., Mahecha, M. D., & Camps-Valls, G. (2025). Sub-seasonal forest carbon dynamics lose persistence under extremes. Environmental Research Letters, 20 (8), 084052. https://iopscience.iop.org/article/10.1088/1748-9326/ade8ff/meta
Habershon, S., Nenoff, K., Kraemer, G., Schüler, L., Zozmann, H., Calabrese, J., Attinger, S., & Mahecha, M. (2025). The spatiotemporal dynamics of COVID-19 in Europe: time-series clustering maps 5 distinct trajectories to spatial patterns. Population Health Metrics, 23, doi:10.1186/s12963-025-00405-w. https://link.springer.com/article/10.1186/s12963-025-00405-w
Sterzik, A., Krone, M., Baum, D., Cunningham, D. W., & Lawonn, K. (2025). Uncertainty Visualization for Biomolecular Structures: An Empirical Evaluation. IEEE Transactions on Visualization and Computer Graphics. https://ieeexplore.ieee.org/abstract/document/11118311/
Eulzer, P., & Lawonn, K. (2025). Uniform parametric mapping of saccular intracranial aneurysms for statistical analysis of morphological variation. Computers & Graphics, 104408. https://www.sciencedirect.com/science/article/pii/S0097849325002493
July 2025 (12)
Liu, G., Ciais, P., Tao, S., Yang, H., & Bastos, A. (2025). A high-resolution, long-term global radar-based above-ground biomass dataset from 1993 to 2020. Earth System Science Data Discussions, 2025, 1-26. https://essd.copernicus.org/preprints/essd-2025-330/
Wang, L., Shi, L., Reimers, C., Wang, Y., He, L., Wang, Y., Reichstein, M., & Jiang, S. (2025). A self-supervised deep learning model for enhanced generalization in soil moisture prediction. Journal of Hydrology, 133974. https://www.sciencedirect.com/science/article/pii/S0022169425013125
Agel, L., Cohen, J., Barlow, M., Pfeiffer, K., Francis, J., Garfinkel, C. I., & Kretschmer, M. (2025). Cold-air outbreaks in the continental US: Connections with stratospheric variations. Science Advances, 11 (28), eadq9557. https://www.science.org/doi/abs/10.1126/sciadv.adq9557
Gyuleva, G., Knutti, R., & Sippel, S. (2025). Combination of internal variability and forced response reconciles observed 2023–2024 warming. Geophysical Research Letters, 52 (14), e2025GL115270. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2025GL115270
Mindlin, J., Shepherd, T. G., Osman, M., Vera, C. S., & Kretschmer, M. (2025). Explaining and predicting the Southern Hemisphere eddy-driven jet. Proceedings of the National Academy of Sciences, 122 (29), e2500697122. https://www.pnas.org/doi/abs/10.1073/pnas.2500697122
Sickert, S., Gogolev, M., Penzel, N., Büchner, T., & Denzler, J. (2025). Modifying Generative Distributions in Latent Diffusion Models to Improve Alignment with Desired Properties. NA, 1-6. https://ieeexplore.ieee.org/abstract/document/11175117/
Seidel, P., Kaufmann, T., & Wolfers, T. (2025). Predicting Developmental Norms from Baseline Cortical Thickness in Longitudinal Studies. bioRxiv. https://pmc.ncbi.nlm.nih.gov/articles/PMC12324185/
Zhan, W., Lian, X., Liu, J., Han, J., Huang, Y., Yang, H., Zhan, C., Winkler, A. J., & Gentine, P. (2025). Reduced water loss rather than increased photosynthesis controls CO2-enhanced water-use efficiency. Nature Ecology & Evolution, 9 (9), 1571-1584. https://www.nature.com/articles/s41559-025-02761-0
Migliavacca, M., Grassi, G., Bastos, A., Ceccherini, G., Ciais, P., Janssens-Maenhout, G., Lugato, E., Mahecha, M. D., Novick, K. A., Peñuelas, J., Pilli, R., Reichstein, M., Avitabile, V., Beck, P. S., Barredo, J. I., Forzieri, G., Herold, M., Korosuo, A., Mansuy, N., Mubareka, S., Orth, R., Rougieux, P., & Cescatti, A. (2025). Securing the forest carbon sink for the European Union’s climate ambition. NA, 643 (8074), 1203-1213. https://www.nature.com/articles/s41586-025-08967-3
Camps-Valls, G. (2025). Serendipity’s role in advancing geoscience. NA, NA (available at https://t.co/sl95oAeToV), 1-2. https://www.nature.com/articles/s41561-025-01748-7
Debeire, K., Gerhardus, A., Bichler, R., Runge, J., & Eyring, V. (2025). Uncertainty bounds for long-term causal effects of perturbations in spatiotemporal systems. Environmental Data Science, 4, e33. https://www.cambridge.org/core/journals/environmental-data-science/article/uncertainty-bounds-for-longterm-causal-effects-of-perturbations-in-spatiotemporal-systems/C1C9C7DBFA2072F82FAD28AF9DE8857F
Liu, J., Wang, Q., Zhan, W., Lian, X., & Gentine, P. (2025). When and where soil dryness matters to ecosystem photosynthesis. Nature Plants, 11 (7), 1390-1400. https://www.nature.com/articles/s41477-025-02024-7
June 2025 (10)
Kahlmeyer, P., Merk, N., & Giesen, J. (2025). Discovering symmetries of ODEs by symbolic regression. CoRR, abs/2506.19550. https://arxiv.org/abs/2506.19550
Zhao, J., Sun, S., Yin, Y., Tang, Y., Li, C., Liang, Y., Wang, Y., Winkler, A., & Jiang, S. (2025). Advancing evapotranspiration modeling with optimized soil and canopy resistance combinations. Water Resources Research, 61 (6), e2024WR039252. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2024WR039252
Zhang, M., Fernández-Torres, M., Cohrs, K., & Camps-Valls, G. (2025). Calibration and uncertainty quantification for deep learning-based drought detection. International Journal of Applied Earth Observation and Geoinformation, 140, 104563. https://www.sciencedirect.com/science/article/pii/S1569843225002109
Büchner, T., Anders, C., Guntinas-Lichius, O., & Denzler, J. (2025). Electromyography-Informed Facial Expression Reconstruction for Physiological-Based Synthesis and Analysis. NA, 215-227. https://openaccess.thecvf.com/content/CVPR2025/html/Buchner_Electromyography-Informed_Facial_Expression_Reconstruction_for_Physiological-Based_Synthesis_and_Analysis_CVPR_2025_paper.html
Lian, X., Li, Y., Liu, J., Kornhuber, K., & Gentine, P. (2025). Northern ecosystem productivity reduced by Rossby-wave-driven hot–dry conditions. Nature Geoscience, 1-9. https://www.nature.com/articles/s41561-025-01722-3
Li, S., Zheng, T., Farchi, A., Bocquet, M., & Gentine, P. (2025). Probabilistic data assimilation for ensemble distribution projections with generative machine learning: A Lorenz’96 proof‐of‐concept. Geophysical Research Letters, 52 (12), e2024GL112523. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2024GL112523
Gackstetter, D., Yu, K., & Körner, M. (2025). Self-attention and frequency-augmentation for unsupervised domain adaptation in satellite image-based time series classification. ISPRS Journal of Photogrammetry and Remote Sensing, 224, 113-132. https://www.sciencedirect.com/science/article/pii/S0924271625001224
Zhao, L., Zhang, Y., Chakraborty, T., Mazumdar, P., Zhang, K., & Gentine, P. (2025). Transfer learning reveals large discrepancies between air and land surface temperatures in cities. NA. https://www.researchsquare.com/article/rs-6665951/latest
Bonnet, P., Pastori, L., Schwabe, M., Giorgetta, M., Iglesias-Suarez, F., & Eyring, V. (2025). Tuning the ICON-A 2.6. 4 climate model with machine-learning-based emulators and history matching. Geoscientific Model Development, 18 (12), 3681-3706. https://gmd.copernicus.org/articles/18/3681/2025/
Kahlmeyer, P., Fischer, M., & Giesen, J. (2025). Dimension reduction for symbolic regression. CoRR, abs/2506.19537. https://arxiv.org/abs/2506.19537
May 2025 (9)
De, R., Bao, S., Koirala, S., Brenning, A., Reichstein, M., Tagesson, T., Liddell, M., Ibrom, A., Wolf, S., Šigut, L., Hörtnagl, L., Woodgate, W., Korkiakoski, M., Merbold, L., Black, T. A., Roland, M., Klosterhalfen, A., Blanken, P. D., Knox, S., Sabbatini, S., Gielen, B., Montagnani, L., Fensholt, R., Wohlfahrt, G., Desai, A. R., Paul-Limoges, E., Galvagno, M., Hammerle, A., Jocher, G., Ruiz Reverter, B., Holl, D., Chen, J., Vitale, L., Arain, M. A., Carvalhais, N., et al. (2025). Addressing challenges in simulating inter–annual variability of gross primary production. Journal of Advances in Modeling Earth Systems, 17, e2024MS004697. https://doi.org/10.1029/2024MS004697
Poehls, J., Meuschke, M., Carvalhais, N., & Lawonn, K. (2025). Either Or: Interactive Articles or Videos for Climate Science Communication. Computer Graphics Forum, e70129. https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.70129
Baghirov, Z., Jung, M., Reichstein, M., Körner, M., & Kraft, B. (2025). H2MV (v1. 0): global physically constrained deep learning water cycle model with vegetation. Geoscientific Model Development, 18 (10), 2921-2943. https://gmd.copernicus.org/articles/18/2921/2025/
Zahoransky, B., Günther, T., & Lawonn, K. (2025). PrismBreak: Exploration of Multi‐Dimensional Mixture Models. Computer Graphics Forum, e70121. https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.70121
Venkataramanan, A., Bodesheim, P., & Denzler, J. (2025). Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models. 41st Conference on Uncertainty in Artificial Intelligence (UAI). https://arxiv.org/abs/2505.05163
Fischer, E. M., Bador, M., Huser, R., Kendon, E. J., Robinson, A., & Sippel, S. (2025). Record-breaking extremes in a warming climate. NA, 1-15. https://www.nature.com/articles/s43017-025-00681-y
Korsch, D., Shadaydeh, M., & Denzler, J. (2025). Simplified Concrete Dropout-Improving the Generation of Attribution Masks for Fine-grained Classification: International Journal of Computer Vision, 1-15. https://link.springer.com/article/10.1007/s11263-025-02453-z
Sterzik, A., Gillmann, C., Krone, M., & Lawonn, K. (2025). Uncertainty‐Aware Visualization of Biomolecular Structures. Computer Graphics Forum, e70155. https://diglib.eg.org/bitstreams/39edbb9f-47b9-4095-98d6-b02b8c4e3504/download
Münch, J. L., Schmauder, R., Paul, F., & Habeck, M. (2025). Using Bayesian priors to overcome non-identifiablility issues in Hidden Markov models. bioRxiv, 2024.04. 20.590387. https://pmc.ncbi.nlm.nih.gov/articles/PMC12247640/
April 2025 (11)
Kahlmeyer, P., Merk, N., & Giesen, J. (2025). Discovering symmetries of ODEs by symbolic regression. AAAI 2025, 17715–17723. https://ojs.aaai.org/index.php/AAAI/article/view/33948
Kahlmeyer, P., Fischer, M., & Giesen, J. (2025). Dimension reduction for symbolic regression. AAAI 2025, 17707–17714. https://ojs.aaai.org/index.php/AAAI/article/view/33947
Debeire, K., Bock, L., Nowack, P., Runge, J., & Eyring, V. (2025). Constraining uncertainty in projected precipitation over land with causal discovery. Earth System Dynamics, 16 (2), 607-630. https://esd.copernicus.org/articles/16/607/2025/
Han, D., Mohamed, S., Li, Y., & Denzler, J. (2025). Diffusion-based Identity-Preserving Facial Privacy Protection. NA, 1-5. https://ieeexplore.ieee.org/abstract/document/10890829/
Ma, C., Li, H., Jiang, J., Aybar, C., Yao, J., & Camps-Valls, G. (2025). Dynamics of Masked Image Modeling in Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing. https://ieeexplore.ieee.org/abstract/document/10950388/
Bourriz, M., Hajji, H., Laamrani, A., Elbouanani, N., Abdelali, H. A., Bourzeix, F., El-Battay, A., Amazirh, A., & Chehbouni, A. (2025). Integration of Hyperspectral Imaging and AI Techniques for Crop Type Mapping: Present Status, Trends, and Challenges. NA, 17 (9), 1574. https://www.mdpi.com/2072-4292/17/9/1574
Wahl, J., & Runge, J. (2025). Separation-Based Distance Measures for Causal Graphs. Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, PMLR, NA (258), p. 3412-3420.. https://arxiv.org/abs/2402.04952
Behrens, G., Beucler, T., Iglesias‐Suarez, F., Yu, S., Gentine, P., Pritchard, M., Schwabe, M., & Eyring, V. (2025). Simulating atmospheric processes in Earth system models and quantifying uncertainties with deep learning multi‐member and stochastic parameterizations. Journal of Advances in Modeling Earth Systems, 17 (4), e2024MS004272. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2024MS004272
Reuss, J., Macdonald, J., Becker, S., Richter, L., & Körner, M. (2025). The EuroCropsML time series benchmark dataset for few-shot crop type classification in Europe. Scientific Data, 12 (1), 664. https://www.nature.com/articles/s41597-025-04952-7
Winkler, A. J., & Sierra, C. A. (2025). Towards a new generation of impulse‐response functions for integrated Earth system understanding and climate change attribution. Geophysical Research Letters, 52 (8), e2024GL112295. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2024GL112295
Immorlano, F., Eyring, V., Gouville, T. l. M. d., Accarino, G., Elia, D., Mandt, S., Aloisio, G., & Gentine, P. (2025). Transferring climate change physical knowledge. Proceedings of the National Academy of Sciences, 122 (15), e2413503122. https://www.pnas.org/doi/abs/10.1073/pnas.2413503122
March 2025 (7)
Fang, J., Lian, X., Ryu, Y., Jeong, S., Jiang, C., & Gentine, P. (2025). A long-term reconstruction of a global photosynthesis proxy over 1982–2023. Scientific data, 12 (1), 372. https://www.nature.com/articles/s41597-025-04686-6
Vemuri, S. K., & Denzler, J. (2025). F-INR: Functional Tensor Decomposition for Implicit Neural Representations. arXiv e-prints, arXiv: 2503.21507. https://ui.adsabs.harvard.edu/abs/2025arXiv250321507K/abstract
Suesse, T., & Brenning, A. (2025). Softening the criteria for determining inner and outer predicted exceedance sets. Spatial Statistics, 65, 100876. https://www.sciencedirect.com/science/article/pii/S2211675324000678
Kraulich, F., Pfleiderer, P., & Sippel, S. (2025). The Impact of Aerosol Forcing on the Statistical Attribution of Heatwaves. Available at SSRN 5227152. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5227152
Wijayawardhana, A., Gunawan, D., & Suesse, T. (2025). Variational Bayes inference for simultaneous autoregressive models with missing data. Statistics and Computing, 35 (3), 68. https://link.springer.com/article/10.1007/s11222-025-10590-1
Prusina, T., & Laue, S. (2024). Efficient Line Search Method Based on Regression and Uncertainty Quantification. NA, 333-341. https://link.springer.com/chapter/10.1007/978-3-031-75623-8_26
Reichstein, R., Benson, V., Blunk, J, Camps-Valls, G., Creutzig, F., Fearnley, C. J., Han, B., Kornhuber, K., Rahaman, N., Schölkopf, B., Tárraga, J. M., Vinuesa, R., Dall, K., Denzler, J., Frank, D., Martini, G., Nganga, N., Maddix, D. C., & Weldemariam, K. (2030). Early warning of complex climate risk with integrated artificial intelligence. Framework, 2015, 2. https://www.nature.com/articles/s41467-025-57640-w
February 2025 (8)
Hsu, W., Meuschke, M., Frangi, A. F., Preim, B., & Lawonn, K. (2025). A survey of intracranial aneurysm detection and segmentation. NA, 103493. https://www.sciencedirect.com/science/article/pii/S1361841525000416
Camps-Valls, G., Fernández-Torres, M., Cohrs, K., Höhl, A., Castelletti, A., Pacal, A., Robin, C., Martinuzzi, F., Papoutsis, I., Prapas, I., Pérez-Aracil, J., Weigel, K., Gonzalez-Calabuig, M., Reichstein, M., Rabel, M., Giuliani, M., Mahecha, M. D., Popescu, O., Pellicer-Valero, O. J., Ouala, S., Salcedo-Sanz, S., Sippel, S., Kondylatos, S., Happé, T., & Williams, T. (2025). Artificial intelligence for modeling and understanding extreme weather and climate events. NA, 16 (1), 1919. https://www.nature.com/articles/s41467-025-56573-8
Benson, V., Bastos, A., Reimers, C., Winkler, A. J., Yang, F., & Reichstein, M. (2025). Atmospheric Transport Modeling of CO2 With Neural Networks. Journal of Advances in Modeling Earth Systems, 17 (2), e2024MS004655. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2024MS004655
Kraft, B., Schirmer, M., Aeberhard, W. H., Zappa, M., Seneviratne, S. I., & Gudmundsson, L. (2025). CH-RUN: a deep-learning-based spatially contiguous runoff reconstruction for Switzerland. Hydrology and Earth System Sciences, 29 (4), 1061-1082. https://hess.copernicus.org/articles/29/1061/2025/
Montero, D., Kraemer, G., Anghelea, A., Aybar, C., Brandt, G., Camps-Valls, G., Cremer, F., Flik, I., Gans, F., Habershon, S., Ji, C., Kattenborn, T., Martínez-Ferrer, L., Martinuzzi, F., Reinhardt, M., Söchting, M., Teber, K., & Mahecha, M. D. (2025). Earth System Data Cubes: Avenues for advancing Earth system research. Environmental Data Science, 3, e27. https://www.cambridge.org/core/journals/environmental-data-science/article/earth-system-data-cubes-avenues-for-advancing-earth-system-research/C49F497A29699C7A1A6A2830755CAA6D
Gonzalez-Calabuig, M., Fernández-Torres, M., & Camps-Valls, G. (2025). Generative networks for spatio-temporal gap filling of Sentinel-2 reflectances. ISPRS Journal of Photogrammetry and Remote Sensing, 220, 637-648. https://www.sciencedirect.com/science/article/pii/S0924271625000152
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Pelucchi, P., Servera, J. V., Stier, P., & Camps-Valls, G. (2025). Invertible Neural Networks for Probabilistic Aerosol Optical Depth Retrieval. IEEE Transactions on Geoscience and Remote Sensing. https://ieeexplore.ieee.org/abstract/document/10883027/
January (16)
Schneider, M., Gackstetter, D., Prexl, J., Meyer, S. T., & Körner, M. (2025). Advancing transnational assessments of biodiversity drivers in European agriculture with an updated hierarchical crop and agriculture taxonomy (HCAT). npj Sustainable Agriculture, 3 (1), 3. https://www.nature.com/articles/s44264-024-00037-x
Mankovich, N., Bouabid, S., Nowack, P., Bassotto, D., & Camps-Valls, G. (2025). Analyzing climate scenarios using dynamic mode decomposition with control. Environmental Data Science, 4, e16. https://www.cambridge.org/core/journals/environmental-data-science/article/analyzing-climate-scenarios-using-dynamic-mode-decomposition-with-control/8DBB4214FC3A3F7CF2359DCFC7AD72E4
Büchner, T., Sickert, S., Volk, G. F., Guntinas-Lichius, O., & Denzler, J. (2025). Assessing 3D volumetric asymmetry in facial palsy patients via advanced multi-view landmarks and radial curves. Machine Vision and Applications, 36 (1), 1. https://link.springer.com/article/10.1007/s00138-024-01616-1
Kraft, B., Schirmer, M., Aeberhard, W. H., Zappa, M., Seneviratne, S. I., & Gudmundsson, L. (2025). CH-RUN: Runoff reconstruction for Switzerland (CHRUN_v1). NA. https://www.research-collection.ethz.ch/handle/20.500.11850/714281
Ji, C., Fincke, T., Benson, V., Camps-Valls, G., Fernández-Torres, M., Gans, F., Kraemer, G., Martinuzzi, F., Montero, D., Mora, K., Pellicer-Valero, O. J., Robin, C., Söchting, M., Weynants, M., & Mahecha, M. D. (2025). DeepExtremeCubes: Earth system spatio-temporal data for assessing compound heatwave and drought impacts. Scientific Data, 12 (1), 149. https://www.nature.com/articles/s41597-025-04447-5
Prusina, T., & Laue, S. (2025). Efficient Line Search Method Based on Regression and Uncertainty. Learning and Intelligent Optimization: 18th International Conference, LION 18, Ischia Island, Italy, June 9–13, 2024, Revised Selected Papers, 14990, 333. https://books.google.com/books?hl=en&lr=&id=wes7EQAAQBAJ&oi=fnd&pg=PA333&dq=info:onWem2JIxT8J:scholar.google.com&ots=j9RfWeM71P&sig=MWXeaef44jGhplPxZuWDd_nvOjA
Chen, S., Huang, J., Huang, J., Wang, P., Sun, C., Zhang, Z., & Jiang, S. (2025). Explainable deep learning identifies patterns and drivers of freshwater harmful algal blooms. Environmental Science and Ecotechnology, 23, 100522. https://www.sciencedirect.com/science/article/pii/S2666498424001364
Pfeifer, C., Knetsch, S., Maercker, J., Mustafa, O., Rümmler, M., & Brenning, A. (2025). Exploring the potential of aerial drone imagery to distinguish breeding Adélie (Pygoscelis adeliae), chinstrap (Pygoscelis antarcticus) and gentoo (Pygoscelis papua) penguins …. Ecological Indicators, 170, 113011. https://www.sciencedirect.com/science/article/pii/S1470160X24014687
Palazzoli, I., Ceola, S., & Gentine, P. (2025). GRAiCE: reconstructing terrestrial water storage anomalies with recurrent neural networks. Scientific Data, 12 (1), 146. https://www.nature.com/articles/s41597-025-04403-3
Martínez-Ferrer, L., Moreno-Martínez, Á., Muñoz-Marí, J., Izquierdo-Verdiguier, E., García-Haro, F. J., & Camps-Valls, G. (2025). High-resolution biophysical parameter estimation at scale with uncertainties. NA, 35-58. https://www.sciencedirect.com/science/article/pii/B9780443299919000021
Nakad, M., Weng, E., & Gentine, P. (2025). Impact of sucrose sinks on phloem transport. bioRxiv, 2025.01. 28.635320. https://www.biorxiv.org/content/10.1101/2025.01.28.635320.abstract
Cohrs, K., Diaz, E., Sitokonstantinou, V., Varando, G., & Camps-Valls, G. (2025). Large language models for causal hypothesis generation in science. Machine Learning: Science and Technology, 6 (1), 013001. https://iopscience.iop.org/article/10.1088/2632-2153/ada47f/meta
Saha, S., & Gawlikowski, J. (2025). Multi-sensor Earth observation: outlook. NA, 409-424. https://www.sciencedirect.com/science/article/pii/B9780443264849000270
Buch, J., Lamb, K. D., & Gentine, P. (2025). Optimizing Cloud Seeding with a Denoising Diffusion Model. 105th Annual AMS Meeting 2025, 105, 448270. https://ui.adsabs.harvard.edu/abs/2025AMS…10548270B/abstract
Liao, K., Buch, J., Lamb, K. D., & Gentine, P. (2025). Simulating the air quality impact of prescribed fires using graph neural network-based PM2. 5 forecasts. Environmental Data Science, 4, e11. https://www.cambridge.org/core/journals/environmental-data-science/article/simulating-the-air-quality-impact-of-prescribed-fires-using-graph-neural-networkbased-pm25-forecasts/A93DD21899CA00208E0DE3F3B0C4DC3C
Miralles, D. G., Arellano, J. V. d., McVicar, T. R., & Mahecha, M. D. (2025). Vegetation-climate feedbacks across scales. Annals of the New York Academy of Sciences, doi:10.1111/nyas.15286. https://nyaspubs.onlinelibrary.wiley.com/doi/abs/10.1111/nyas.15286
2024 (105)
December (7)
Wijayawardhana, A., Gunawan, D., & Suesse, T. (2024). A marginal maximum likelihood approach for hierarchical simultaneous autoregressive models with missing data. Mathematics, 12 (23), 3870. https://www.mdpi.com/2227-7390/12/23/3870
Nathaniel, J., Qu, Y., Nguyen, T., Yu, S., Busecke, J., Grover, A., & Gentine, P. (2024). Chaosbench: A multi-channel, physics-based benchmark for subseasonal-to-seasonal climate prediction. Advances in Neural Information Processing Systems, 37, 43715-43729. https://proceedings.neurips.cc/paper_files/paper/2024/hash/4d3684dd7926754b48bc6cd99a840232-Abstract-Datasets_and_Benchmarks_Track.html
Grundner, A., Beucler, T., Gentine, P., & Eyring, V. (2024). Data‐driven equation discovery of a cloud cover parameterization. Journal of Advances in Modeling Earth Systems, 16 (3), e2023MS003763. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2023MS003763
Fang, J., & Gentine, P. (2024). Exploring Optimal Complexity for Water Stress Representation in Terrestrial Carbon Models: A Hybrid‐Machine Learning Model Approach. Journal of Advances in Modeling Earth Systems, 16 (12), e2024MS004308. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2024MS004308
Büchner, T., Penzel, N., Guntinas-Lichius, O., & Denzler, J. (2024). Facing Asymmetry-Uncovering the Causal Link Between Facial Symmetry and Expression Classifiers Using Synthetic Interventions. NA, 4100-4121. https://openaccess.thecvf.com/content/ACCV2024/html/Buchner_Facing_Asymmetry_-_Uncovering_the_Causal_Link_between_Facial_Symmetry_ACCV_2024_paper.html
Schäfer, S., & Brenning, A. (2024). Industry diversity in entrepreneurial ecosystems–A longitudinal study of industrial composition and firm locations in Tel Aviv, Israel. Progress in Economic Geography, 2 (2), 100016. https://www.sciencedirect.com/science/article/pii/S2949694224000105
Kühbacher, B., Iglesias-Suarez, F., Kilbertus, N., & Eyring, V. (2024). Towards physically consistent deep learning for climate model parameterizations. NA, 280-287. https://ieeexplore.ieee.org/abstract/document/10903227/
November (8)
Venkataramanan, A., Kloster, M., Burfeid-Castellanos, A., Dani, M., Mayombo, N. A., Vidakovic, D., Langenkämper, D., Tan, M., Pradalier, C., Nattkemper, T., Laviale, M., & Beszteri, B. (2024). “UDE DIATOMS in the Wild 2024”: a new image dataset of freshwater diatoms for training deep learning models. GigaScience, 13, giae087. https://academic.oup.com/gigascience/article-abstract/doi/10.1093/gigascience/giae087/7912108
Lockwood, J. W., Gori, A., & Gentine, P. (2024). A generative super‐resolution model for enhancing tropical cyclone wind field intensity and resolution. Journal of Geophysical Research: Machine Learning and Computation, 1 (4), e2024JH000375. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2024JH000375
Cortes-Andres, J., Fernandez-Torres, M., & Camps-Valls, G. (2024). Deep learning with noisy labels for spatio-temporal drought detection. IEEE Transactions on Geoscience and Remote Sensing. https://ieeexplore.ieee.org/abstract/document/10759777/
Sippel, S., Kent, E. C., Meinshausen, N., Chan, D., Kadow, C., Neukom, R., Fischer, E. M., Humphrey, V., Rohde, R., Vries, I. d., & Knutti, R. (2024). Early-twentieth-century cold bias in ocean surface temperature observations. Nature, 635 (8039), 618-624. https://www.nature.com/articles/s41586-024-08230-1
Schratz, P., Becker, M., Lang, M., & Brenning, A. (2024). Mlr3spatiotempcv: Spatiotemporal resampling methods for machine learning in R. Journal of Statistical Software, 111, 1-36. https://www.jstatsoft.org/article/view/v111i07
Gier, B. K., Schlund, M., Friedlingstein, P., Jones, C. D., Jones, C., Zaehle, S., & Eyring, V. (2024). Representation of the terrestrial carbon cycle in CMIP6. Biogeosciences, 21 (22), 5321-5360. https://bg.copernicus.org/articles/21/5321/2024/
Gawlikowski, J., Kasieczka, G., Segal, G., & Gan, H. (2024). Using AI for Radio (Big) Data. Data-Intensive Radio Astronomy: Bringing Astrophysics to the Exabyte Era, 472, 251. https://books.google.com/books?hl=en&lr=&id=_bUvEQAAQBAJ&oi=fnd&pg=PA251&dq=info:aqa00G1ssv8J:scholar.google.com&ots=zftv9oL69x&sig=WmvRkObIjM9Fc0NRfoS6tZSx1mU
Hombeck, J., Voigt, H., & Lawonn, K. (2024). Voice user interfaces for effortless navigation in medical virtual reality environments. Computers & Graphics, 124, 104069. https://www.sciencedirect.com/science/article/pii/S0097849324002048
October (7)
Zhong, L., Lei, H., Li, Z., & Jiang, S. (2024). Advancing streamflow prediction in data-scarce regions through vegetation-constrained distributed hybrid ecohydrological models. Journal of Hydrology, 132165. https://www.sciencedirect.com/science/article/pii/S0022169424015610
Ahmad, W., Kasburg, V., Kukowski, N., Shadaydeh, M., & Denzler, J. (2024). Deep‐learning based causal inference: A feasibility study based on three years of tectonic‐climate data from Moxa geodynamic observatory. Earth and Space Science, 11 (10), e2023EA003430. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2023EA003430
Teber, K., Sippel, S., Krause, M., Zscheischler, J., & Mahecha, M. (2024). Inequality in human development amplifies climate-related disaster risk. NA. https://www.researchsquare.com/article/rs-5331763/latest
Li, S., Qu, Y., Zheng, T., & Gentine, P. (2024). Machine‐assisted physical closure for coarse suspended sediments in vegetated turbulent channel flows. Geophysical Research Letters, 51 (20), e2024GL110475. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2024GL110475
Han, D., Jiang, Y., Li, Y., Mendes, R., & Denzler, J. (2024). Robust skin color driven privacy-preserving face recognition via function secret sharing. NA, 3965-3971. https://ieeexplore.ieee.org/abstract/document/10647630/
Gomarasca, U., Duveiller, G., Pacheco-Labrador, J., Ceccherini, G., Cescatti, A., Girardello, M., Nelson, J. A., Reichstein, M., Wirth, C., & Migliavacca, M. (2024). Satellite remote sensing reveals the footprint of biodiversity on multiple ecosystem functions across the NEON eddy covariance network. Environmental Research: Ecology, 3 (4), 045003. https://iopscience.iop.org/article/10.1088/2752-664X/ad87f9/meta
Kim, S., Nathaniel, J., Hou, Z., Zheng, T., & Gentine, P. (2024). Spatiotemporal upscaling of sparse air-sea pCO2 data via physics-informed transfer learning. Scientific data, 11 (1), 1098. https://www.nature.com/articles/s41597-024-03959-w
September (9)
Kretschmer, M., Jézéquel, A., Labe, Z. M., & Touma, D. (2024). A shifting climate: New paradigms and challenges for (early career) scientists in extreme weather research. Atmospheric Science Letters, 25 (11), e1268. https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/asl.1268
Eyring, V., Gentine, P., Camps-Valls, G., Lawrence, D. M., & Reichstein, M. (2024). AI-empowered next-generation multiscale climate modelling for mitigation and adaptation. NA, 17 (10), 963-971. https://www.nature.com/articles/s41561-024-01527-w
Tuia, D., Schindler, K., Demir, B., Zhu, X. X., Kochupillai, M., Džeroski, S., Rijn, J. N. v., Hoos, H. H., Frate, F. D., Datcu, M., Markl, V., Saux, B. L., Schneider, R., & Camps-Valls, G. (2024). Artificial Intelligence to Advance Earth Observation: A review of models, recent trends, and pathways forward. NA. https://ieeexplore.ieee.org/abstract/document/10669817/
Tárraga, J. M., Sevillano-Marco, E., Muñoz-Marí, J., Piles, M., Sitokonstantinou, V., Ronco, M., Miranda, M. T., Cerdà, J., & Camps-Valls, G. (2024). Causal discovery reveals complex patterns of drought-induced displacement. IScience, 27 (9). https://www.cell.com/iscience/fulltext/S2589-0042(24)01853-4
Liu, G., Migliavacca, M., Reimers, C., Kraft, B., Reichstein, M., Richardson, A. D., Wingate, L., Delpierre, N., Yang, H., & Winkler, A. J. (2024). DeepPhenoMem V1. 0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology. Geoscientific Model Development, 17 (17), 6683-6701. https://gmd.copernicus.org/articles/17/6683/2024/
Zhang, M., Fernandez-Torres, M., & Camps-Valls, G. (2024). Domain knowledge-driven variational recurrent networks for drought monitoring. Remote Sensing of Environment, 311, 114252. https://www.sciencedirect.com/science/article/pii/S0034425724002700
Jeong, S., Ryu, Y., Li, X., Dechant, B., Liu, J., Kong, J., Choi, W., Fang, J., Lian, X., & Gentine, P. (2024). GEOSIF: A continental-scale sub-daily reconstructed solar-induced fluorescence derived from OCO-3 and GK-2A over Eastern Asia and Oceania. Remote Sensing of Environment, 311, 114284. https://www.sciencedirect.com/science/article/pii/S003442572400302X
Benedetti, R., Piersimoni, F., Pratesi, M., Salvati, N., & Suesse, T. (2024). Handling Out‐of‐Sample Areas to Estimate the Unemployment Rate at Local Labour Market Areas in Italy. International Statistical Review. https://onlinelibrary.wiley.com/doi/abs/10.1111/insr.12596
Jeong, S., Ryu, Y., & Gentine, P. (2024). Persistent global greening over the last four decades using novel long-term vegetation. NA. https://escholarship.org/content/qt9b87c3rc/qt9b87c3rc.pdf?utm_source=consensus
August (12)
Rump, P. G., Merk, N., Klaus, J., Wenig, M., & Giesen, J. (2024). Convexity certificates for symbolic tensor expressions. IJCAI 2024, 1953–1960. https://dl.acm.org/doi/abs/10.24963/ijcai.2024/216
Winkler, A. J., Myneni, R., Reimers, C., Reichstein, M., & Brovkin, V. (2024). Carbon system state determines warming potential of emissions. Plos one, 19 (8), e0306128. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0306128
Mahecha, M. D., Kraemer, G., & Crameri, F. (2024). Cautionary Remarks on the Planetary Boundary Visualization. NA, 15, 1153-1159. https://esd.copernicus.org/articles/15/1153/2024/
Genin, K., Grote, T., & Wolfers, T. (2024). Computational psychiatry and the evolving concept of a mental disorder. Synthese, 204 (3), 88. https://link.springer.com/article/10.1007/s11229-024-04741-6
Vemuri, S. K., Büchner, T., Niebling, J., & Denzler, J. (2024). Functional Tensor Decompositions for Physics-Informed Neural Networks. arXiv e-prints, arXiv: 2408.13101. https://ui.adsabs.harvard.edu/abs/2024arXiv240813101K/abstract
Skulovich, O., Li, X., Wigneron, J., & Gentine, P. (2024). Global L-band equivalent AI-based vegetation optical depth dataset. Scientific Data, 11 (1), 936. https://www.nature.com/articles/s41597-024-03810-2
Heuer, H., Schwabe, M., Gentine, P., Giorgetta, M. A., & Eyring, V. (2024). Interpretable multiscale machine learning‐based parameterizations of convection for ICON. Journal of Advances in Modeling Earth Systems, 16 (8), e2024MS004398. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2024MS004398
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Ding, W., & Camps-Valls, G. (2024). Overview of ACM SIGKDD 2024 AI4Science4AI Special Day. NA, 6693-6694. https://dl.acm.org/doi/abs/10.1145/3637528.3673871
Eyring, V., Collins, W. D., Gentine, P., Barnes, E. A., Barreiro, M., Beucler, T., Bocquet, M., Bretherton, C. S., Christensen, H. M., Dagon, K., Gagne, D. J., Hall, D., Hammerling, D., Hoyer, S., Iglesias-Suarez, F., Lopez-Gomez, I., Mcgraw, M. C., Meehl, G. A., Molina, M. J., Monteleoni, C., Mueller, J., Pritchard, M. S., Rolnick, D., Runge, J., Stier, P., Watt-Meyer, O., Weigel, K., Yu, R., & Zanna, L. (2024). Pushing the frontiers in climate modelling and analysis with machine learning. NA, 14 (9), 916-928. https://www.nature.com/articles/s41558-024-02095-y
Rangzan, M., Attarchi, S., Gloaguen, R., & Alavipanah, S. K. (2024). SAR temporal shifting: A new approach for optical-to-SAR translation with consistent viewing geometry. Remote Sensing, 16 (16), 2957. https://www.mdpi.com/2072-4292/16/16/2957
Rewicki, F., Gawlikowski, J., Niebling, J., & Denzler, J. (2024). Unraveling anomalies in time: Unsupervised discovery and isolation of anomalous behavior in bio-regenerative life support system telemetry. NA, 207-222. https://link.springer.com/chapter/10.1007/978-3-031-70378-2_13
July (15)
Cerdà-Bautista, J., Tárraga, J. M., Sitokonstantinou, V., & Camps-Valls, G. (2024). Assessing the Causal Impact of Humanitarian Aid on Food Security. NA, 1546-1552. https://ieeexplore.ieee.org/abstract/document/10642230/
Kodgirwar, S., Loetgering, L., Liu, C., Joseph, A., Licht, L., Molina, D. S. P., Eschen, W., Rothhardt, J., & Habeck, M. (2024). Bayesian multi-exposure image fusion for robust high dynamic range ptychography. Optics Express, 32 (16), 28090-28099. https://opg.optica.org/abstract.cfm?uri=oe-32-16-28090
Cohrs, K., Varando, G., Carvalhais, N., Reichstein, M., & Camps-Valls, G. (2024). Causal hybrid modeling with double machine learning—applications in carbon flux modeling. Machine Learning: Science and Technology, 5 (3), 035021. https://iopscience.iop.org/article/10.1088/2632-2153/ad5a60/meta
Blacher, M., Staudt, C., Klaus, J., Wenig, M., Merk, N., Breuer, A., Engel, M., Laue, S., & Giesen, J. (2024). Einsum benchmark: Enabling the development of next-generation tensor execution engines. NeurIPS 2024. https://proceedings.neurips.cc/paper_files/paper/2024/hash/b1bbfdb9197bfc819a52c34dce493f85-Abstract-Datasets_and_Benchmarks_Track.html
Staudt, C., Blacher, M., Klaus, J., Lippmann, F., & Giesen, J. (2024). Improved cut strategy for tensor network contraction orders. SEA 2024, 27:1–27:19. https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2024.27
Sippel, S., Barnes, C., Cadiou, C., Fischer, E., Kew, S., Kretschmer, M., Philip, S., Shepherd, T. G., Singh, J., Vautard, R., & Yiou, P. (2024). Could an extremely cold central European winter such as 1963 happen again despite climate change?. Weather and Climate Dynamics, 5 (3), 943-957. https://wcd.copernicus.org/articles/5/943/2024/
Stein, G., Ziemer, J., Wicker, C., Jänichen, J., Demisch, G., Klöpper, D., Last, K., Denzler, J., Schmullius, C., Shadaydeh, M., & Dubois, C. (2024). Data-Driven Prediction of Large Infrastructure Movements Through Persistent Scatterer Time Series Modeling. NA, 8669-8673. https://ieeexplore.ieee.org/abstract/document/10642253/
Pawellek, M., Rössl, C., & Lawonn, K. (2024). Distance‐Based Smoothing of Curves on Surface Meshes. Computer Graphics Forum, 43 (5), e15135. https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.15135
Tárraga, J. M., Piles, M., Kamateri, E., Marco, E. S., Tsampoulatidis, I., Muñoz-Marí, J., & Camps-Valls, G. (2024). Drought Displacement Forecasts Can Be Improved With Twitter Data. NA, 3984-3987. https://ieeexplore.ieee.org/abstract/document/10642237/
Wahl, J., Ninad, U., & Runge, J. (2024). Foundations of causal discovery on groups of variables. Journal of Causal Inference, 12 (1), 20230041. https://www.degruyterbrill.com/document/doi/10.1515/jci-2023-0041/html
Jiang, S., Sweet, L., Blougouras, G., Brenning, A., Li, W., Reichstein, M., Denzler, J., Shangguan, W., Yu, G., Huang, F., & Zscheischler, J. (2024). How interpretable machine learning can benefit process understanding in the geosciences. Earth’s Future, 12 (7), e2024EF004540. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2024EF004540
Mora, K., Rzanny, M., Wäldchen, J., Feilhauer, H., Kattenborn, T., Kraemer, G., Mäder, P., Svidzinska, D., Wolf, S., & Mahecha, M. D. (2024). Macrophenological dynamics from citizen science plant occurrence data. Methods in Ecology and Evolution, 10.1111/2041-210X.14365. https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.14365
Montero, D., Aybar, C., Ji, C., Kraemer, G., Söchting, M., Teber, K., & Mahecha, M. D. (2024). On-demand earth system data cubes. NA, 7529-7532. https://ieeexplore.ieee.org/abstract/document/10640742/
Engel, M., & Körner, M. (2024). Sentinel-2 Tiling Scheme Grid-Overlay for Efficient I/O-Operations Based on Spherical Voronoi Polygons and Local Optimization. NA, 6895-6899. https://ieeexplore.ieee.org/abstract/document/10640984/
Gonzalez-Calabuig, M., Cortés-Andrés, J., Williams, T. K. E., Zhang, M., Pellicer-Valero, O. J., Fernández-Torres, M., & Camps-Valls, G. (2024). The aide toolbox: Artificial intelligence for disentangling extreme events [software and data sets]. IEEE Geoscience and Remote Sensing Magazine, 12 (2), 113-118. https://ieeexplore.ieee.org/abstract/document/10582507/
June (14)
Mahecha, M., Bastos, A., Bohn, F. J., Eisenhauer, N., Feilhauer, H., Hickler, T., Kalesse-Los, H., Migliavacca, M., Otto, F., Peng, J., Sippel, S., Tegen, I., Weigelt, A., Wendisch, M., Wirth, C., Al-Halbouni, D., Deneke, H., Doktor, D., Dunker, S., Duveiller, G., Ehrlich, A., Foth, A., García-García, A., Guerra, C., Guimarães-Steinicke, C., Hartmann, H., Henning, S., Herrmann, H., Hu, P., Ji, C., Kattenborn, T., Kolleck, N., Kretschmer, M., Kühn, I., Luttkus, M., Maahn, M., Mönks, M., Mora, K., Pöhlker, M., Reichstein, M., Rüger, N., Sánchez-Parra, B., Schäfer, M., Stratmann, F., Tesche, M., Wehner, B., Wieneke, S., Winkler, A., Wolf, S., Zaehle, S., Zscheischler, J., & Quaas, J. (2024). Biodiversity and Climate Extremes: Known Interactions and Research Gaps. Earth’s Future, 12 (6), e2023EF003963. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2023EF003963
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Karmouche, S., Galytska, E., Meehl, G. A., Runge, J., Weigel, K., & Eyring, V. (2024). Changing effects of external forcing on Atlantic–Pacific interactions. Earth System Dynamics, 15 (3), 689-715. https://esd.copernicus.org/articles/15/689/2024/
Kaps, A., Lauer, A., Kazeroni, R., Stengel, M., & Eyring, V. (2024). Characterizing clouds with the CCClim dataset, a machine learning cloud class climatology. Earth System Science Data, 16 (6), 3001-3016. https://essd.copernicus.org/articles/16/3001/2024/essd-16-3001-2024.html
Qu, Y., Nathaniel, J., Li, S., & Gentine, P. (2024). Deep generative data assimilation in multimodal setting. NA, 449-459. https://openaccess.thecvf.com/content/CVPR2024W/EarthVision/html/Qu_Deep _Generative_Data_Assimilation_in_Multimodal_Setting_CVPRW_2024_paper.html
Vemuri, S. K., Büchner, T., & Denzler, J. (2024). Estimating soil hydraulic parameters for unsaturated flow using physics-informed neural networks. NA, 338-351. https://link.springer.com/chapter/10.1007/978-3-031-63759-9_37
Zhan, C., Orth, R., Yang, H., Reichstein, M., Zaehle, S., Kauwe, M. G. D., Rammig, A., & Winkler, A. J. (2024). Estimating the CO2 Fertilization Effect on Extratropical Forest Productivity From Flux‐Tower Observations. Journal of Geophysical Research: Biogeosciences, 129 (6), e2023JG007910. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2023JG007910
Eulzer, P., Richter, K., Hundertmark, A., Wickenhöfer, R., Klingner, C. M., & Lawonn, K. (2024). Instantaneous Visual Analysis of Blood Flow in Stenoses Using Morphological Similarity. Computer Graphics Forum, 43 (3), e15081. https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.15081
Lawonn, K., Meuschke, M., & Günther, T. (2024). Inversevis: Revealing the hidden with curved sphere tracing. Computer Graphics Forum, 43 (3), e15080. https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.15080
Benson, V., Robin, C., Requena-Mesa, C., Alonso, L., Carvalhais, N., Cortés, J., Gao, Z., Linscheid, N., Weynants, M., & Reichstein, M. (2024). Multi-modal learning for geospatial vegetation forecasting. NA, 27788-27799. http://openaccess.thecvf.com/content/CVPR2024/html/Benson_Multi-modal_Learning_for_Geospatial_Vegetation_Forecasting_CVPR_2024_paper.html
Büchner, T., Penzel, N., Guntinas-Lichius, O., & Denzler, J. (2024). The power of properties: Uncovering the influential factors in emotion classification. NA, 440-448. https://link.springer.com/chapter/10.1007/978-981-97-8705-0_32
Giesen, J., Lucas, P., Pfeiffer, L., Schmalwasser, L., & Lawonn, K. (2024). The whole and its parts: Visualizing gaussian mixture models. Visual Informatics, 8 (2), 67-79. https://www.sciencedirect.com/science/article/pii/S2468502X24000196
Wang, S., Yang, H., Koirala, S., Forkel, M., Reichstein, M., & Carvalhais, N. (2024). Understanding disturbance regimes from patterns in modeled forest biomass. Journal of Advances in Modeling Earth Systems, 16 (6), e2023MS004099. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2023MS004099
Feng, J., Li, J., Xu, C., Wang, Z., Zhang, Z., Wu, X., Lai, C., Zeng, Z., Tong, H., & Jiang, S. (2024). Viewing soil moisture flash drought onset mechanism and their changes through XAI lens: A case study in eastern China. Water Resources Research, 60 (6), e2023WR036297. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2023WR036297
May (3)
Yang, H., Andreassen, O., Westlye, L. T., Marquand, A. F., Beckmann, C. F., & Wolfers, T. (2024). Learning cortical anomaly through masked encoding for unsupervised heterogeneity mapping. NA, 1-5. https://ieeexplore.ieee.org/abstract/document/10635183/
Kodgirwar, S., Loetgering, L., Liu, C., Licht, L., Joseph, A., Penagos, D. S., Eschen, W., Rothhardt, J., & Habeck, M. (2024). Probabilistic High Dynamic Range Preprocessing for Ptychography. NA, JTu2A. 69. https://opg.optica.org/abstract.cfm?uri=cleo_fs-2024-JTu2A.69
Shaw, T. A., Leung, L. R., Narsey, S., Martius, O., Seager, R., Shepherd, T. G., Sörensson, A. A., Stephenson, T., Taylor, M., Wang, L., Arias, P. A., Collins, M., Coumou, D., Diedhiou, A., Garfinkel, C. I., Jain, S., Roxy, M. K., & Kretschmer, M. (2024). Regional climate change. NA. https://ul.qucosa.de/id/qucosa:102011
April (1)
Paulus, S. J., Orth, R., Lee, S., Hildebrandt, A., Jung, M., Nelson, J. A., El-Madany, T. S., Carrara, A., Moreno, G., Mauder, M., Groh, J., Graf, A., Reichstein, M., & Migliavacca, M. (2024). Interpretability of negative latent heat fluxes from eddy covariance measurements in dry conditions. Biogeosciences, 21 (8), 2051-2085. https://bg.copernicus.org/articles/21/2051/2024/
March (6)
Robin, C., Benson, V., Requena-Mesa, C., Alonso, L., Poehls, J., Russwurm, M., Carvalhais, N., & Reichstein, M. (2024). Analyzing Spatio-Temporal Machine Learning Models through Input Perturbation. European Geosciences Union General Assembly 2024 (EGU24), 17389. https://ui.adsabs.harvard.edu/abs/2024EGUGA..2617389R/abstract
Debeire, K., Gerhardus, A., Runge, J., & Eyring, V. (2024). Bootstrap aggregation and confidence measures to improve time series causal discovery. NA, 979-1007. https://proceedings.mlr.press/v236/debeire24a.html
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Jiang, S., Tarasova, L., Yu, G., & Zscheischler, J. (2024). Compounding effects in flood drivers challenge estimates of extreme river floods. Science Advances, 10 (13), eadl4005. https://www.science.org/doi/abs/10.1126/sciadv.adl4005
Stein, G., Vemuri, S. K., Huang, Y., Ebeling, A., Eisenhauer, N., Shadaydeh, M., & Denzler, J. (2024). Investigating the effects of plant diversity on soil thermal diffusivity using Physics-Informed Neural Networks. NA. https://openreview.net/pdf?id=7wKTS923Uc
Suesse, T., Steel, D., & Tranmer, M. (2024). The Effects of Omitting Components in a Multilevel Model With Social Network Effects. Sociological Methods & Research, 53 (4), 1976-2018. https://journals.sagepub.com/doi/abs/10.1177/00491241231156972
February (7)
Iglesias‐Suarez, F., Gentine, P., Solino‐Fernandez, B., Beucler, T., Pritchard, M., Runge, J., & Eyring, V. (2024). Causally‐informed deep learning to improve climate models and projections. Journal of Geophysical Research: Atmospheres, 129 (4), e2023JD039202. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2023JD039202
Gerhardus, A. (2024). Characterization of causal ancestral graphs for time series with latent confounders. The Annals of Statistics, 52 (1), 103-130. https://projecteuclid.org/journals/annals-of-statistics/volume-52/issue-1/Characterization-of-causal-ancestral-graphs-for-time-series-with-latent/10.1214/23-AOS2325.short
Porter, R. Z., Huang, Y., & Gentine, P. (2024). Exploring Data-Driven Equation Discovery for the Modeling of Moisture Flux. 104th Annual AMS Meeting 2024, 104, 430266. https://ui.adsabs.harvard.edu/abs/2024AMS…10430266P/abstract
Porter, R. Z., Huang, Y., & Gentine, P. (2024). Exploring Data-Driven Equation Discovery to Model Moisture Flux. 104th Annual AMS Meeting 2024, 104, 429454. https://ui.adsabs.harvard.edu/abs/2024AMS…10429454P/abstract
Lamb, K. D., & Gentine, P. (2024). Exploring Phase Transitions and Dynamical Processes in Tropical Moist Convection Using Machine Learning. 104th Annual AMS Meeting 2024, 104, 437050. https://ui.adsabs.harvard.edu/abs/2024AMS…10437050L/abstract
Kumar, P., Dayan, P., & Wolfers, T. (2024). From Complexity to Precision—Charting Decision-Making Through Normative Modeling. JAMA psychiatry, 81 (2), 117-118. https://jamanetwork.com/journals/jamapsychiatry/article-abstract/2813433
Richter, K., Probst, T., Hundertmark, A., Eulzer, P., & Lawonn, K. (2024). Longitudinal wall shear stress evaluation using centerline projection approach in the numerical simulations of the patient-based carotid artery. Computer Methods in Biomechanics and Biomedical Engineering, 27 (3), 347-364. https://www.tandfonline.com/doi/abs/10.1080/10255842.2023.2185478
January (16)
Camps-Valls, G., Martinez, E., Fablet, R., & Jamet, C. (2024). AI and remote sensing in ocean sciences. NA, 10, 1248591. https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2023.1248591/full
Tomko, M., Xin, Y., & Wahl, J. (2024). Causal Inference for Spatial Data Analytics. NA. https://repository.tudelft.nl/record/uuid:934058f5-da3f-4a11-b8b1-21170292c949
Ahmad, W., Shadaydeh, M., & Denzler, J. (2024). Deep learning-based group causal inference in multivariate time-series. AAAI Workshop on AI for Time-series (AAAI-WS) 2024. https://arxiv.org/abs/2401.08386
Lian, X., Peñuelas, J., Ryu, Y., Piao, S., Keenan, T. F., Fang, J., Yu, K., Chen, A., Zhang, Y., & Gentine, P. (2024). Diminishing carryover benefits of earlier spring vegetation growth. Nature ecology & evolution, 8 (2), 218-228. https://www.nature.com/articles/s41559-023-02272-w
Hu, Y., Jiang, Y., Yao, H., Chen, Y., & Wu, X. (2024). Effects of stacking LSTM with different patterns and input schemes on streamflow and water quality simulation. NA. https://www.researchsquare.com/article/rs-3740192/latest
Besnard, S., Santoro, M., Herold, M., Cartus, O., Gütter, J., Heinrich, V., Herault, B., Kassi, J., Koirala, S., N’Guessan, A., Neigh, C., Nelson, J. A., Poulter, B., Weber, U., Zhang, T., & Carvalhais, N. (2024). Global Age Mapping Integration (GAMI). NA. https://gfzpublic.gfz-potsdam.de/pubman/faces/ViewItemFullPage.jsp?itemId=item_5029361_2
Palazzoli, I., Ceola, S., & Gentine, P. (2024). GRAiCE: terrestrial water storage anomalies reconstructions. NA. https://cris.unibo.it/handle/11585/968429
Son, R., Stacke, T., Gayler, V., Nabel, J. E., Schnur, R., Alonso, L., Requena‐Mesa, C., Winkler, A. J., Hantson, S., Zaehle, S., Weber, U., & Carvalhais, N. (2024). Integration of a Deep‐Learning‐Based Fire Model Into a Global Land Surface Model. Journal of Advances in Modeling Earth Systems, 16 (1), e2023MS003710. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2023MS003710
Chan, A., & Körner, M. (2024). Knowledge Distillation from Big Administrative Data. NA. https://mediatum.ub.tum.de/1778941
Benson, V., Donges, J. F., Boers, N., Hirota, M., Morr, A., Staal, A., Vollmer, J., & Wunderling, N. (2024). Measuring tropical rainforest resilience under non-Gaussian disturbances. Environmental Research Letters, 19 (2), 024029. https://iopscience.iop.org/article/10.1088/1748-9326/ad1e80/meta
Servera, J. V., Martino, L., Verrelst, J., Rivera-Caicedo, J. P., & Camps-Valls, G. (2024). Multioutput feature selection for emulation and sensitivity analysis. IEEE Transactions on Geoscience and Remote Sensing, 62, 1-11. https://ieeexplore.ieee.org/abstract/document/10413599/
Engel, M., Kunz, S., Wetzel, M., & Körner, M. (2024). Multiresolution Analysis-based Assessment of Agricultural Effects on Groundwater Levels. NA. https://mediatum.ub.tum.de/1778940
Marquand, A., Rutherford, S., & Wolfers, T. (2024). Normative modeling for clinical neuroscience. NA, 309-329. https://www.sciencedirect.com/science/article/pii/B9780443134807000144
Varando, G., Catsis, S., Diaz, E., & Camps-Valls, G. (2024). Pairwise causal discovery with support measure machines. Applied Soft Computing, 150, 111030. https://www.sciencedirect.com/science/article/pii/S1568494623010487
Robin, C., Weynants, M., Benson, V., Carvalhais, N., Rußwurm, M., & Reichstein, M. (2024). Spatially far, Ecologically close: Evaluating Extrapolation on Vegetation Forecasting Models. Proc. 2nd ML4RS Workshop, 1-10. https://edepot.wur.nl/660514
Xie, Y., Büchner, T., Schuhmann, L., Guntinas-Lichius, O., & Denzler, J. (2024). Unsupervised learning of eye state prototypes for semantically rich blinking detection. NA, 1607-1611. https://pub.inf-cv.uni-jena.de/pdf/xie2024unsupervised.pdf
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December (12)
Nikolic, S., Grundy, S., Haque, R., Lal, S., Hassan, G. M., Daniel, S. A., Belkina, M., Lyden, S., & Suesse, T. (2023). A ranking comparison of the traditional, online and mixed laboratory mode learning objectives in engineering: Uncovering different priorities. STEM Education, 3 (4), 331-349. https://research.usc.edu.au/esploro/outputs/journalArticle/A-ranking-comparison-of-the-traditional/99984497202621
Liu, Y., Le, S., Zou, Y., Sadgedhi, M., Chen, Y., Andela, N., & Gentine, P. (2023). A simplified machine learning based wildfire ignition model from insurance perspective. ICLR 2023 Workshop on Tackling Climate Change with Machine Learning. https://www.climatechange.ai/papers/iclr2023/23/paper.pdf
Venkataramanan, A. (2023). Automatic identification of diatoms using deep learning to improve ecological diagnosis of aquatic environments. NA. https://theses.hal.science/tel-04691855/
Camps-Valls, G., Gerhardus, A., Ninad, U., Varando, G., Martius, G., Balaguer-Ballester, E., Vinuesa, R., Diaz, E., Zanna, L., & Runge, J. (2023). Discovering causal relations and equations from data. NA, 1044, 1-68. https://www.sciencedirect.com/science/article/pii/S0370157323003411
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Schneider, M., Gackstetter, D., Prexl, J., Meyer, S. T., & Körner, M. (2023). Towards Understanding the Impact of European Agriculture on Biodiversity with an Updated Hierarchical Crop and Agriculture Taxonomy (HCAT). NA. https://www.researchgate.net/profile/Maja-Schneider-6/publication/376445623_Towards_Understanding_the_Impact_of_European_Agriculture_on_Biodiversity_with_an_Updated_Hierarchical_Crop_and_Agriculture_Taxonomy_HCAT/links/6579ad20fc4b416622c220f9/Towards-Understanding-the-Impact-of-European-Agriculture-on-Biodiversity-with-an-Updated-Hierarchical-Crop-and-Agriculture-Taxonomy-HCAT.pdf
Wang, Z., & Brenning, A. (2023). Unsupervised active–transfer learning for automated landslide mapping. Computers & Geosciences, 181, 105457. https://www.sciencedirect.com/science/article/pii/S0098300423001619
November (2)
Tian, L., Disse, M., & Huang, J. (2023). Drought cascades across multiple systems in Central Asia identified based on the dynamic space-time motion approach. Hydrology and Earth System Sciences, 2023, 1-28. https://hess.copernicus.org/articles/27/4115/2023/hess-27-4115-2023.html
Klaus, J., Merk, N., Wiedom, K., Laue, S., & Giesen, J. (2022). Convexity certificates from Hessians. NeurIPS 2022. https://proceedings.neurips.cc/paper_files/paper/2022/hash/2e0802e2898522a0ab8858ca8831a206-Abstract-Conference.html
October (15)
Penzel, N., Kierdorf, J., Roscher, R., & Denzler, J. (2023). Analyzing the behavior of cauliflower harvest-readiness models by investigating feature relevances. NA, 572-581. https://ieeexplore.ieee.org/abstract/document/10350727/
Rougieux, P., Patani, S., & Migliavacca, M. (2023). Biotrade: A Python package to access and analyse the international trade of bio-based products. Journal of Open Source Software, 8 (90), 5550. https://joss.theoj.org/papers/10.21105/joss.05550.pdf
Koskina, A., Plionis, M., Papoutsis, I., & Camps-Valls, G. (2023). Earth observation as a tool to assess climate migration and policy-making: Legal aspects. E3S Web of Conferences, 436, 02005. https://www.e3s-conferences.org/articles/e3sconf/abs/2023/73/e3sconf_iced2023_02005/e3sconf_iced2023_02005.html
Büchner, T., Sickert, S., Volk, G. F., Guntinas-Lichius, O., & Denzler, J. (2023). From faces to volumes-measuring volumetric asymmetry in 3D facial palsy scans. NA, 121-132. https://link.springer.com/chapter/10.1007/978-3-031-47969-4_10
Vemuri, S. K., & Denzler, J. (2023). Gradient statistics-based multi-objective optimization in physics-informed neural networks. Sensors, 23 (21), 8665. https://www.mdpi.com/1424-8220/23/21/8665
Brenning, A., Bastos, A., Koirala, S., Balsamo, G., Reichstein, M., & Orth, R. (2023). Impact of Updating Vegetation Information on Land Surface Model Performance. Journal of Geophysical Research: Atmospheres, 10. https://pure.mpg.de/rest/items/item_3506757_6/component/file_3548335/content?download=true
Fan, N., Santoro, M., Besnard, S., Cartus, O., Koirala, S., & Carvalhais, N. (2023). Implications of the steady-state assumption for the global vegetation carbon turnover. Environmental Research Letters, 18 (10), 104036. https://iopscience.iop.org/article/10.1088/1748-9326/acfb22/meta
Sterzik, A., Lichtenberg, N., Wilms, J., Krone, M., Cunningham, D. W., & Lawonn, K. (2023). Perception of line attributes for visualization. IEEE Transactions on Visualization and Computer Graphics, 30 (1), 1041-1051. https://ieeexplore.ieee.org/abstract/document/10292709/
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Suesse, T., Brenning, A., & Grupp, V. (2023). Spatial linear discriminant analysis approaches for remote-sensing classification. Spatial Statistics, 57, 100775. https://www.sciencedirect.com/science/article/pii/S2211675323000507
Körschens, M., Bucher, S. F., Römermann, C., & Denzler, J. (2023). Unified Automatic Plant Cover and Phenology Prediction. NA, 685-693. https://openaccess.thecvf.com/content/ICCV2023W/CVPPA/html/Korschens_Unified_Automatic_Plant_Cover_and_Phenology_Prediction_ICCVW_2023_paper.html
Büchner, T., Sickert, S., Graßme, R., Anders, C., Guntinas-Lichius, O., & Denzler, J. (2023). Using 2d and 3d face representations to generate comprehensive facial electromyography intensity maps. NA, 136-147. https://link.springer.com/chapter/10.1007/978-3-031-47966-3_11
Chan, A. X., Schneider, M., & Körner, M. (2023). XAI for Early Crop Classification. NA. https://ieeexplore.ieee.org/abstract/document/10281498/
Lamb, K. D., & Gentine, P. (2023). Zero-shot learning of aerosol optical properties with graph neural networks. Scientific Reports, 13 (1), 18777. https://www.nature.com/articles/s41598-023-45235-8
Klaus, J., Merk, N., Wiedom, K., Laue, S., & Giesen, J. (2022). Convexity certificates from Hessians. CoRR, abs/2210.10430. https://arxiv.org/abs/2210.10430
September (8)
Gentine, P. (2023). Cross-Scale Land-Atmosphere Experiment (CSLAEX). NA, NA (DOE-COLUMBIA-0014203). https://www.osti.gov/biblio/1923011
Paçal, A., Hassler, B., Weigel, K., Kurnaz, M. L., Wehner, M. F., & Eyring, V. (2023). Detecting extreme temperature events using Gaussian mixture models. Journal of Geophysical Research: Atmospheres, 128 (18), e2023JD038906. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2023JD038906
Schneider, M., Schelte, T., Schmitz, F., & Körner, M. (2023). EuroCrops: The Largest Harmonized Open Crop Dataset Across the European Union. Scientific Data, 10 (1), 612. https://www.nature.com/articles/s41597-023-02517-0
Galytska, E., Weigel, K., Handorf, D., Jaiser, R., Köhler, R., Runge, J., & Eyring, V. (2023). Evaluating causal Arctic‐midlatitude teleconnections in CMIP6. Journal of Geophysical Research: Atmospheres, 128 (17), e2022JD037978. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2022JD037978
Li, L., Wang, J., Franklin, M., Yin, Q., Wu, J., Camps-Valls, G., Zhu, Z., Wang, C., Ge, Y., & Reichstein, M. (2023). Improving air quality assessment using physics-inspired deep graph learning. npj Climate and Atmospheric Science, 6 (1), 152. https://www.nature.com/articles/s41612-023-00475-3
Venkataramanan, A., Laviale, M., & Pradalier, C. (2023). Integrating visual and semantic similarity using hierarchies for image retrieval. NA, 422-431. https://link.springer.com/chapter/10.1007/978-3-031-44137-0_35
Bhouri, M. A., Peng, L., Pritchard, M. S., & Gentine, P. (2023). Multi-fidelity climate model parameterization for better generalization and extrapolation. arXiv e-prints, arXiv: 2309.10231. https://ui.adsabs.harvard.edu/abs/2023arXiv230910231A/abstract
Dosio, A., Spinoni, J., & Migliavacca, M. (2023). Record-breaking and unprecedented compound hot and dry summers in Europe under different emission scenarios. Environmental Research: Climate, 2 (4), 045009. https://iopscience.iop.org/article/10.1088/2752-5295/acfa1b/meta
August (7)
Sterzik, A., Lichtenberg, N., Krone, M., Baum, D., Cunningham, D. W., & Lawonn, K. (2023). Enhancing molecular visualization: Perceptual evaluation of line variables with application to uncertainty visualization. Computers & Graphics, 114, 401-413. https://www.sciencedirect.com/science/article/pii/S009784932300095X
Celik, M. F., Isik, M. S., Taskin, G., Erten, E., & Camps-Valls, G. (2023). Explainable artificial intelligence for cotton yield prediction with multisource data. IEEE Geoscience and Remote Sensing Letters, 20, 1-5. https://ieeexplore.ieee.org/abstract/document/10214067/
Büchner, T., Guntinas-Lichius, O., & Denzler, J. (2023). Improved obstructed facial feature reconstruction for emotion recognition with minimal change cyclegans. NA, 262-274. https://link.springer.com/chapter/10.1007/978-3-031-45382-3_22
Panwar, A., Migliavacca, M., Nelson, J. A., Cortés, J., Bastos, A., Forkel, M., & Winkler, A. J. (2023). Methodological challenges and new perspectives of shifting vegetation phenology in eddy covariance data. Scientific Reports, 13 (1), 13885. https://www.nature.com/articles/s41598-023-41048-x
Servera, J. V., Martino, L., Verrelst, J., & Camps-Valls, G. (2023). Multifidelity Gaussian process emulation for atmospheric radiative transfer models. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-10. https://ieeexplore.ieee.org/abstract/document/10198286/
Li, X., Ryu, Y., Xiao, J., Dechant, B., Liu, J., Li, B., Jeong, S., & Gentine, P. (2023). New-generation geostationary satellite reveals widespread midday depression in dryland photosynthesis during 2020 western US heatwave. Science Advances, 9 (31), eadi0775. https://www.science.org/doi/abs/10.1126/sciadv.adi0775
Bao, S., Alonso, L., Wang, S., Gensheimer, J., De, R., & Carvalhais, N. (2023). Toward Robust Parameterizations in Ecosystem‐Level Photosynthesis Models. Journal of Advances in Modeling Earth Systems, 15 (8), e2022MS003464. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2022MS003464
July (13)
Zou, Y., Sadeghi, M., Liu, Y., Puchko, A., Le, S., Chen, Y., Andela, N., & Gentine, P. (2023). Attention-based wildland fire spread modeling using fire-tracking satellite observations. Fire, 6 (8), 289. https://www.mdpi.com/2571-6255/6/8/289
Habeck, M. (2023). Bayesian methods in integrative structure modeling. NA, 404 (8-9), 741-754. https://www.degruyterbrill.com/document/doi/10.1515/hsz-2023-0145/html
Celik, M. F., Isik, M. S., Erten, E., & Camps-Valls, G. (2023). Explainability of end and mid-season cotton yield predictors in ConUS. NA, 3538-3541. https://ieeexplore.ieee.org/abstract/document/10283252/
Giesen, J., Kahlmeyer, P., Nussbaum, F., & Zarrieß, S. (2022). Leveraging the Wikipedia Graph for Evaluating Word Embeddings. IJCAI 2022, 4136–4142. https://www.ijcai.org/proceedings/2022/0574.pdf
Reuss, J., Engel, M., Tumampos, S., & Körner, M. (2023). Generalization Across Sensor-Modalities for Deforestation Assessment. NA, 1361-1364. https://ieeexplore.ieee.org/abstract/document/10281799/
Jiang, Y., Bao, X., Huang, Z., Chen, Y., Wu, X., Li, X., Wu, X., & Hu, Y. (2023). Identification of pollutant delivery processes during different storm events and hydrological years in a semi-arid mountainous reservoir basin. Science of the Total Environment, 883, 163606. https://www.sciencedirect.com/science/article/pii/S0048969723022258
Brunner, L., & Sippel, S. (2023). Identifying climate models based on their daily output using machine learning. Environmental Data Science, 2, e22. https://www.cambridge.org/core/journals/environmental-data-science/article/identifying-climate-models-based-on-their-daily-output-using-machine-learning/0D2453720761810E4382709E0BEC62CE
Penzel, N., & Denzler, J. (2023). Interpreting Art by Leveraging Pre-Trained Models. NA, 1-6. https://ieeexplore.ieee.org/abstract/document/10216010/
Gomarasca, U., Migliavacca, M., Kattge, J., Nelson, J. A., Niinemets, Ü., Wirth, C., Cescatti, A., Bahn, M., Nair, R., Acosta, A. T., Arain, M. A., Beloiu, M., Black, T. A., Bruun, H. H., Bucher, S. F., Buchmann, N., Byun, C., Carrara, A., Conte, A., Silva, A. C. D., Duveiller, G., Fares, S., Ibrom, A., Knohl, A., Komac, B., Limousin, J., Lusk, C. H., Mahecha, M. D., Martini, D., Minden, V., Montagnani, L., Mori, A. S., Onoda, Y., Peñuelas, J., Perez-Priego, O., Poschlod, P., Powell, T. L., Reich, P. B., Šigut, L., Bodegom, P. M. V., Walther, S., Wohlfahrt, G., Wright, I. J., & Reichstein, M. (2023). Leaf-level coordination principles propagate to the ecosystem scale. Nature Communications, 14 (1), 3948. https://www.nature.com/articles/s41467-023-39572-5
Díaz, E., Varando, G., Johnson, J. E., & Camps-Valls, G. (2023). Learning latent functions for causal discovery. Machine Learning: Science and Technology, 4 (3), 035004. https://iopscience.iop.org/article/10.1088/2632-2153/ace151/meta
Bhouri, M. A., & Gentine, P. (2023). Memory-based parameterization with differentiable solver: Application to Lorenz’96. Chaos: An Interdisciplinary Journal of Nonlinear Science, 33 (7). https://pubs.aip.org/aip/cha/article/33/7/073116/2901140
Nathaniel, J., Liu, J., & Gentine, P. (2023). MetaFlux: Meta-learning global carbon fluxes from sparse spatiotemporal observations. Scientific Data, 10 (1), 440. https://www.nature.com/articles/s41597-023-02349-y
Qing, Y., Wang, S., Yang, Z., & Gentine, P. (2023). Soil moisture− atmosphere feedbacks have triggered the shifts from drought to pluvial conditions since 1980. Communications Earth & Environment, 4 (1), 254. https://www.nature.com/articles/s43247-023-00922-2
June (13)
Blacher, M., Klaus, J., Staudt, C., Laue, S., Leis, V., & Giesen, J. (2023). Efficient and portable Einstein summation in SQL. Proceedings of the ACM on Management of Data, 1(2), 121:1–121:19. https://dl.acm.org/doi/abs/10.1145/3589266
Klaus, J., Blacher, M., Goral, A., Lucas, P., & Giesen, J. (2023). A visual analytics workflow for probabilistic modeling. *Visual Informatics, 7*(2), 72–84. https://www.sciencedirect.com/science/article/pii/S2468502X23000153
Eulzer, P., Deylen, F. v., Hsu, W., Wickenhöfer, R., Klingner, C. M., & Lawonn, K. (2023). A fully integrated pipeline for visual carotid morphology analysis. Computer graphics forum, 42 (3), 25-37. https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.14808
Klaus, J., Blacher, M., & Giesen, J. (2023). Compiling tensor expressions into einsum. ICCS 2023 (2), 129–136. https://link.springer.com/chapter/10.1007/978-3-031-36021-3_10
Taskin, G., Yetkin, E. F., & Camps-Valls, G. (2023). A scalable unsupervised feature selection with orthogonal graph representation for hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-13. https://ieeexplore.ieee.org/abstract/document/10147263/
Blougouras, G., Philippopoulos, K., & Tzanis, C. G. (2023). An extreme wind speed climatology–Atmospheric driver identification using neural networks. Science of The Total Environment, 875, 162590. https://www.sciencedirect.com/science/article/pii/S0048969723012068
Runge, J., Gerhardus, A., Varando, G., Eyring, V., & Camps-Valls, G. (2023). Causal inference for time series. NA, 4 (7), 487-505. https://www.nature.com/articles/s43017-023-00431-y
Svendsen, D. H., Hernandez-Lobato, D., Martino, L., Laparra, V., Moreno-Martinez, A., & Camps-Valls, G. (2023). Inference over radiative transfer models using variational and expectation maximization methods. Machine Learning, 112 (3), 921-937. https://link.springer.com/article/10.1007/s10994-021-05999-4
Kariyathan, T., Bastos, A., Marshall, J., Peters, W., Tans, P., & Reichstein, M. (2023). Reducing errors on estimates of the carbon uptake period based on time series of atmospheric CO. Atmospheric Measurement Techniques, 16 (12), 3299-3312. https://amt.copernicus.org/articles/16/3299/2023/
Buch, J., Williams, A. P., Juang, C. S., Hansen, W. D., & Gentine, P. (2023). SMLFire1. 0: a stochastic machine learning (SML) model for wildfire activity in the western United States. Geoscientific Model Development, 16 (12), 3407-3433. https://gmd.copernicus.org/articles/16/3407/2023/
Wahl, J., Ninad, U., & Runge, J. (2023). Vector causal inference between two groups of variables. Proceedings of the AAAI Conference on Artificial Intelligence, 37 (10), 12305-12312. https://ojs.aaai.org/index.php/AAAI/article/view/26450
Preßler, R., Meuschke, M., Voigt, H., & Lawonn, K. (2023). WEB-ANEULYSIS: A web-based application for the analysis of aneurysm data. NA, 88-94. https://link.springer.com/chapter/10.1007/978-3-658-41657-7_21
Mitterreiter, M., Koch, M., Giesen, J., & Laue, S. (2023). Why capsule neural networks do not scale: Challenging the dynamic parse-tree assumption. Proceedings of the AAAI Conference on Artificial Intelligence, 37 (8), 9209-9216. https://ojs.aaai.org/index.php/AAAI/article/view/26104
May (7)
Giesen, J., Kahlmeyer, P., Laue, S., Mitterreiter, M., Nussbaum, F., & Staudt, C. (2023). Mixed membership Gaussians. Journal of Multivariate Analysis, 195, 105141. https://doi.org/10.1016/j.jmva.2022.105141
Migliavacca, M., Gu, L., Woods, J. D., & Wohlfahrt, G. (2023). Editorial special issue: Advancing foundational sun-induced chlorophyll fluorescence science. Agricultural and Forest Meteorology, 337. https://www.osti.gov/biblio/2203796
Gawlikowski, J., Saha, S., Niebling, J., & Zhu, X. X. (2023). Handling unexpected inputs: incorporating source-wise out-of-distribution detection into SAR-optical data fusion for scene classification. EURASIP Journal on Advances in Signal Processing, 2023 (1), 47. https://link.springer.com/article/10.1186/s13634-023-01008-z
Shamekh, S., Lamb, K. D., Huang, Y., & Gentine, P. (2023). Implicit learning of convective organization explains precipitation stochasticity. Proceedings of the National Academy of Sciences, 120 (20), e2216158120. https://www.pnas.org/doi/abs/10.1073/pnas.2216158120
Kim, M., Leonardsen, E., Kjelkenes, R., Rutherford, S., Wang, Y., Alnaes, D., Marquand, A., Westlye, L. T., Moberget, T., & Wolfers, T. (2023). Machine Learning for Parsing Individual Differences. Biological Psychiatry, 93 (9), S53. https://www.biologicalpsychiatryjournal.com/article/S0006-3223(23)00221-4/abstract
Beucler, T., Tam, F. I., Gomez, M. S., Runge, J., & Gerhardus, A. (2023). Selecting robust features for machine-learning applications using multidata causal discovery. Environmental Data Science, 2, e27. https://www.cambridge.org/core/journals/environmental-data-science/article/selecting-robust-features-for-machinelearning-applications-using-multidata-causal-discovery/29C08A0FF7BFD2347768F315E041A143
Preim, B., Raidou, R., Smit, N., & Lawonn, K. (2023). Visualization, visual analytics and virtual reality in medicine: State-of-the-art Techniques and Applications. NA. https://books.google.com/books?hl=en&lr=&id=iyipEAAAQBAJ&oi=fnd&pg=PP1&dq=info:2JWWAzfUe68J:scholar.google.com&ots=IZixeVMG-E&sig=6eEaKspG2UWrjEyUF5L1muomVh4
April (5)
Bastos, A., Sippel, S., Frank, D., Mahecha, M. D., Zaehle, S., Zscheischler, J., & Reichstein, M. (2023). A joint framework for studying compound ecoclimatic events. NA, 4 (5), 333-350. https://www.nature.com/articles/s43017-023-00410-3
Peña, M. A., & Brenning, A. (2023). Benchmarking Sentinel-2-derived predictors for long-term burn severity modelling: the 2016–17 Chilean firestorm. International Journal of Remote Sensing, 44 (8), 2668-2690. https://www.tandfonline.com/doi/abs/10.1080/01431161.2023.2205981
Xi, X., Zhuang, Q., Kim, S., & Gentine, P. (2023). Evaluating the effects of precipitation and evapotranspiration on soil moisture variability within CMIP5 using SMAP and ERA5 data. Water Resources Research, 59 (5), e2022WR034225. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2022WR034225
Huang, F., Zhang, Y., Zhang, Y., Shangguan, W., Li, Q., Li, L., & Jiang, S. (2023). Interpreting Conv-LSTM for spatio-temporal soil moisture prediction in China. Agriculture, 13 (5), 971. https://www.mdpi.com/2077-0472/13/5/971
Brenning, A. (2023). Interpreting machine-learning models in transformed feature space with an application to remote-sensing classification. Machine Learning, 112 (9), 3455-3471. https://link.springer.com/article/10.1007/s10994-023-06327-8
March (8)
Skulovich, O., & Gentine, P. (2023). A long-term consistent artificial intelligence and remote sensing-based soil moisture dataset. Scientific data, 10 (1), 154. https://www.nature.com/articles/s41597-023-02053-x
Gawlikowski, J., Tassi, C. R. N., Ali, M., Lee, J., Humt, M., Feng, J., Kruspe, A., Triebel, R., Jung, P., Roscher, R., Shahzad, M., Yang, W., Bamler, R., & Zhu, X. X. (2023). A survey of uncertainty in deep neural networks. Artificial Intelligence Review, 56 (Suppl 1), 1513-1589. https://link.springer.com/article/10.1007/s10462-023-10562-9
Benson, V., Mesa, C. R., Robin, C., Alonso, L., Cortés, J., Gao, Z., Linscheid, N., Weynants, M., & Reichstein, M. (2023). Forecasting localized weather impacts on vegetation as seen from space with meteo-guided video prediction. NA. https://pure.mpg.de/rest/items/item_3509273/component/file_3509274/content
Papanikolaou, N., Lambiotte, R., & Vaccario, G. (2023). Fragmentation from group interactions: A higher-order adaptive voter model. Physica A: Statistical Mechanics and its Applications, 630 (129257). https://www.sciencedirect.com/science/article/pii/S0378437123008129
ElGhawi, R., Kraft, B., Reimers, C., Reichstein, M., Körner, M., Gentine, P., & Winkler, A. J. (2023). Hybrid modeling of evapotranspiration: inferring stomatal and aerodynamic resistances using combined physics-based and machine learning. Environmental Research Letters, 18 (3), 034039. https://iopscience.iop.org/article/10.1088/1748-9326/acbbe0/meta
Karmouche, S., Galytska, E., Runge, J., Meehl, G. A., Phillips, A. S., Weigel, K., & Eyring, V. (2023). Regime-oriented causal model evaluation of Atlantic–Pacific teleconnections in CMIP6. Earth System Dynamics, 14 (2), 309-344. https://esd.copernicus.org/articles/14/309/2023/
Brenning, A. (2023). Spatial machine-learning model diagnostics: a model-agnostic distance-based approach. International Journal of Geographical Information Science, 37 (3), 584-606. https://www.tandfonline.com/doi/abs/10.1080/13658816.2022.2131789
Hombeck, J., Voigt, H., Heggemann, T., Datta, R. R., & Lawonn, K. (2023). Tell me where to go: Voice-controlled hands-free locomotion for virtual reality systems. NA, 123-134. https://ieeexplore.ieee.org/abstract/document/10108085/
February (5)
Hoffmann, S., & Lessig, C. (2023). Atmodist: Self-supervised representation learning for atmospheric dynamics. Environmental Data Science, 2, e6. https://www.cambridge.org/core/journals/environmental-data-science/article/atmodist-selfsupervised-representation-learning-for-atmospheric-dynamics/877CE83199C921B57C7966AF412C4272
Li, X., Piao, S., Huntingford, C., Peñuelas, J., Yang, H., Xu, H., Chen, A., Friedlingstein, P., Keenan, T. F., Sitch, S., Wang, X., Zscheischler, J., & Mahecha, M. D. (2023). Global variations in critical drought thresholds that impact vegetation. National Science Review, 10 (5), nwad049. https://academic.oup.com/nsr/article-abstract/10/5/nwad049/7057875
Mateo-Sanchis, A., Adsuara, J. E., Piles, M., Munoz-Marí, J., Perez-Suay, A., & Camps-Valls, G. (2023). Interpretable long short-term memory networks for crop yield estimation. IEEE Geoscience and Remote Sensing Letters, 20, 1-5. https://ieeexplore.ieee.org/abstract/document/10041987/
Zhou, A., Hawkins, L., & Gentine, P. (2023). Proof-of-concept: Using ChatGPT to Translate and Modernize an Earth System Model from Fortran to Python/JAX. NeurIPS 2023, NA (Tackling Climate Change with Machine Lea). https://arxiv.org/abs/2405.00018
Bueso, D., Piles, M., Ciais, P., Wigneron, J., Moreno-Martínez, Á., & Camps-Valls, G. (2023). Soil and vegetation water content identify the main terrestrial ecosystem changes. National Science Review, 10 (5), nwad026. https://academic.oup.com/nsr/article-abstract/10/5/nwad026/7030907
January (9)
Wang, Q., Moreno-Martínez, Á., Muñoz-Marí, J., Campos-Taberner, M., & Camps-Valls, G. (2023). Estimation of vegetation traits with kernel NDVI. ISPRS Journal of Photogrammetry and Remote Sensing, 195, 408-417. https://www.sciencedirect.com/science/article/pii/S0924271622003343
Perez-Suay, A., Gordaliza, P., Loubes, J., Sejdinovic, D., & Camps-Valls, G. (2023). Fair kernel regression through cross-covariance operators. Transactions on Machine Learning Research. https://openreview.net/forum?id=MyQ1e1VQQ3
Venkataramanan, A., Benbihi, A., Laviale, M., & Pradalier, C. (2023). Gaussian latent representations for uncertainty estimation using mahalanobis distance in deep classifiers. NA, 4488-4497. https://openaccess.thecvf.com/content/ICCV2023W/UnCV/html/Venkataramanan_Gaussian_Latent_Representations_for_Uncertainty_Estimation_Using_Mahalanobis_Distance_in_ICCVW_2023_paper.html
Schneider, F., Sickert, S., Brandes, P., Marshall, S., & Denzler, J. (2023). Hard is the Task, the Samples are Few: A German Chiasmus Dataset. Language Technology Conference: Human Language Technologies as a Challenge for Computer Science and Linguistics (LTC). Poznan, Poland. https://pub.inf-cv.uni-jena.de/pdf/schneider2023hard.pdf
Körschens, M., Bucher, S. F., Römermann, C., & Denzler, J. (2023). Improving Data Efficiency for Plant Cover Prediction with Label Interpolation and Monte-Carlo Cropping. NA, 321-334. https://link.springer.com/chapter/10.1007/978-3-031-54605-1_21
Kaps, A., Lauer, A., Camps-Valls, G., Gentine, P., Gómez-Chova, L., & Eyring, V. (2023). Machine-learned cloud classes from satellite data for process-oriented climate model evaluation. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-15. https://ieeexplore.ieee.org/abstract/document/10025016/
Giesen, J., Kuehne, L., & Laue, S. (2023). The GENO Software Stack. NA, 213-228. https://link.springer.com/chapter/10.1007/978-3-031-21534-6_12
Venkataramanan, A., Faure-Giovagnoli, P., Regan, C., Heudre, D., Figus, C., Usseglio-Polatera, P., Pradalier, C., & Laviale, M. (2023). Usefulness of synthetic datasets for diatom automatic detection using a deep-learning approach. Engineering Applications of Artificial Intelligence, 117, 105594. https://www.sciencedirect.com/science/article/pii/S095219762200584X
Eulzer, P., Richter, K., Hundertmark, A., Meuschke, M., Wickenhöfer, R., Klingner, C. M., & Lawonn, K. (2023). Visualizing Carotid Stenoses for Stroke Treatment and Prevention. NA. https://diglib.eg.org/server/api/core/bitstreams/b7be348a-bc12-4c81-b84d-f47fe263a3cc/content
2022 (88)
December (6)
Grundner, A., Beucler, T., Gentine, P., Iglesias‐Suarez, F., Giorgetta, M. A., & Eyring, V. (2022). Deep learning based cloud cover parameterization for ICON. Journal of Advances in Modeling Earth Systems, 14 (12), e2021MS002959. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2021MS002959
Fan, N., Reichstein, M., Koirala, S., Ahrens, B., Mahecha, M. D., & Carvalhais, N. (2022). Global apparent temperature sensitivity of terrestrial carbon turnover modulated by hydrometeorological factors. Nature Geoscience, 15 (12), 989-994. https://www.nature.com/articles/s41561-022-01074-2
Voigt, H., Meuschke, M., Zarrieß, S., & Lawonn, K. (2022). KeywordScape: Visual document exploration using contextualized keyword embeddings. NA, 137-147. https://aclanthology.org/2022.emnlp-demos.14/
Lian, X., Zhao, W., & Gentine, P. (2022). Recent global decline in rainfall interception loss due to altered rainfall regimes. Nature Communications, 13 (1), 7642. https://www.nature.com/articles/s41467-022-35414-y
Paulus, S. J., El-Madany, T. S., Orth, R., Hildebrandt, A., Wutzler, T., Carrara, A., Moreno, G., Perez-Priego, O., Kolle, O., Reichstein, M., & Migliavacca, M. (2022). Resolving seasonal and diel dynamics of non-rainfall water inputs in a Mediterranean ecosystem using lysimeters. Hydrology and Earth System Sciences, 26 (23), 6263-6287. https://hess.copernicus.org/articles/26/6263/2022/hess-26-6263-2022.html
Jiang, S., Bevacqua, E., & Zscheischler, J. (2022). River flooding mechanisms and their changes in Europe revealed by explainable machine learning. Hydrology and Earth System Sciences, 26, 6339–6359. https://hess.copernicus.org/articles/26/6339/2022/hess-26-6339-2022.html
November (5)
Mahecha, M. D., Bastos, A., Bohn, F. J., Eisenhauer, N., Feilhauer, H., Hartmann, H., Hickler, T., Kalesse-Los, H., Migliavacca, M., Otto, F. E. L., Peng, J., Quaas, J., Tegen, I., Weigelt, A., Wendisch, M., & Wirth, C. (2022). Biodiversity loss and climate extremes — study the feedbacks. Nature, 621, 30-32. https://www.nature.com/articles/d41586-022-04152-y
Zechlau, S., Schlund, M., Cox, P. M., Friedlingstein, P., & Eyring, V. (2022). Do emergent constraints on carbon cycle feedbacks hold in CMIP6?. Journal of Geophysical Research: Biogeosciences, 127 (12), e2022JG006985. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2022JG006985
Gawlikowski, J., Ebel, P., Schmitt, M., & Zhu, X. X. (2022). Explaining the Effects of Clouds on Remote Sensing Scene Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 9976-9986. https://ieeexplore.ieee.org/abstract/document/9956865/
Schneider, F., Sickert, S., Brandes, P., Marshall, S., & Denzler, J. (2022). Metaphor detection for low resource languages: From zero-shot to few-shot learning in Middle High German. NA, 75-80. https://aclanthology.org/2022.mwe-1.11/
Bao, S., Ibrom, A., Wohlfahrt, G., Koirala, S., Migliavacca, M., Zhang, Q., & Carvalhais, N. (2022). Narrow but robust advantages in two-big-leaf light use efficiency models over big-leaf light use efficiency models at ecosystem level. Agricultural and Forest Meteorology, 326, 109185. https://www.sciencedirect.com/science/article/pii/S0168192322003720
October (7)
Büchner, T., Sickert, S., Volk, G. F., Guntinas-Lichius, O., & Denzler, J. (2022). Automatic objective severity grading of peripheral facial palsy using 3D radial curves extracted from point clouds. NA, 179-183. https://ebooks.iospress.nl/doi/10.3233/SHTI220433
Bhouri, M. A., & Gentine, P. (2022). History-Based, Bayesian, Closure for Stochastic Parameterization: Application to Lorenz’96. arXiv e-prints, arXiv: 2210.14488. https://ui.adsabs.harvard.edu/abs/2022arXiv221014488A/abstract
Zhan, W., Lian, X., Liu, J., & Gentine, P. (2022). Inappropriateness of space-for-time and variability-for-time approaches to infer future dryland productivity changes. Frontiers in Environmental Science, 10, 1010269. https://www.frontiersin.org/articles/10.3389/fenvs.2022.1010269/full
Penzel, N., Reimers, C., Bodesheim, P., & Denzler, J. (2022). Investigating neural network training on a feature level using conditional independence. NA, 383-399. https://link.springer.com/chapter/10.1007/978-3-031-25075-0_27
Abdulkadir, A., Bathula, D. R., Dvornek, N. C., Habes, M., Kia, S. M., Kumar, V., & Wolfers, T. (2022). Machine Learning in Clinical Neuroimaging: 5th International Workshop, MLCN 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings. NA, 13596. https://books.google.com/books?hl=en&lr=&id=NPiTEAAAQBAJ&oi=fnd&pg=PR5&dq=info:AVJJBoeDWE4J:scholar.google.com&ots=rlqmR-bpev&sig=l85dGnpzFMx82nbQ0-MZ4FyjD8k
Martínez-Ferrer, L., Moreno-Martínez, Á., Campos-Taberner, M., García-Haro, F. J., Muñoz-Marí, J., Running, S. W., Kimball, J., Clinton, N., & Camps-Valls, G. (2022). Quantifying uncertainty in high resolution biophysical variable retrieval with machine learning. Remote Sensing of Environment, 280, 113199. https://www.sciencedirect.com/science/article/pii/S0034425722003091
Camps-Valls, G., Campos-Taberner, M., Laparra, V., Martino, L., & Munoz-Mari, J. (2022). Retrieval of Physical Parameters With Deep Structured Kernel Regression. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-10. https://ieeexplore.ieee.org/abstract/document/9910959/
September (13)
Venkataramanan, A., Richard, A., & Pradalier, C. (2022). A data driven approach to generate realistic 3D tree barks. Graphical Models, 123, 101166. https://www.sciencedirect.com/science/article/pii/S152407032200042X
Yu, X., Orth, R., Reichstein, M., Bahn, M., Klosterhalfen, A., Knohl, A., Koebsch, F., Migliavacca, M., Mund, M., Nelson, J. A., Stocker, B. D., Walther, S., & Bastos, A. (2022). Contrasting drought legacy effects on gross primary productivity in a mixed versus pure beech forest. Biogeosciences, 19 (17), 4315-4329. https://bg.copernicus.org/articles/19/4315/2022/bg-19-4315-2022.html
Zhou, S., Williams, A. P., Lintner, B. R., Findell, K. L., Keenan, T. F., Zhang, Y., & Gentine, P. (2022). Diminishing seasonality of subtropical water availability in a warmer world dominated by soil moisture–atmosphere feedbacks. Nature communications, 13 (1), 5756. https://www.nature.com/articles/s41467-022-33473-9
Zhan, C., Orth, R., Migliavacca, M., Zaehle, S., Reichstein, M., Engel, J., Rammig, A., & Winkler, A. J. (2022). Emergence of the physiological effects of elevated CO2 on land–atmosphere exchange of carbon and water. Global Change Biology, 28 (24), 7313-7326. https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.16397
Schlund, M., Hassler, B., Lauer, A., Andela, B., Jöckel, P., Kazeroni, R., Tomas, S. L., Medeiros, B., Predoi, V., Sénési, S., Servonnat, J., Stacke, T., Vegas-Regidor, J., Zimmermann, K., & Eyring, V. (2022). Evaluation of native Earth system model output with ESMValTool v2. 6.0. Geoscientific Model Development Discussions, 2022, 1-28. https://gmd.copernicus.org/articles/16/315/2023/
Pacheco-Labrador, J., Weber, U., Ma, X., Mahecha, M. D., Carvalhais, N., Wirth, C., Huth, A., Bohn, F. J., Kraemer, G., Heiden, U., & Migliavacca, M. (2022). Evalutating the potential of desis to infer plant taxonomical and functional diversities in europwean forests. NA, 49-55. https://pure.mpg.de/rest/items/item_3431856/component/file_3431857/content
Yin, J., Slater, L., Gu, L., Liao, Z., Guo, S., & Gentine, P. (2022). Global increases in lethal compound heat stress: hydrological drought hazards under climate change. Geophysical Research Letters, 49 (18), e2022GL100880. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2022GL100880
Lawonn, K., Meuschke, M., Eulzer, P., Mitterreiter, M., Giesen, J., & Günther, T. (2022). GRay: Ray casting for visualization and interactive data exploration of Gaussian mixture models. IEEE Transactions on Visualization and Computer Graphics, 29 (1), 526-536. https://ieeexplore.ieee.org/abstract/document/9903677/
Sterzik, A., Lichtenberg, N., Krone, M., Cunningham, D. W., & Lawonn, K. (2022). Perceptual Evaluation of Common Line Variables for Displaying Uncertainty on Molecular Surfaces.. NA, 41-51. https://diglib.eg.org/bitstream/handle/10.2312/vcbm20221186/041-051.pdf
Trifunov, V. T., Shadaydeh, M., & Denzler, J. (2022). Time Series Causal Link Estimation under Hidden Confounding using Knockoff Interventions. arXiv e-prints, arXiv: 2209.11497. https://ui.adsabs.harvard.edu/abs/2022arXiv220911497T/abstract
Wahl, J. (2022). Traces on diagram algebras II: centralizer algebras of easy groups and new variations of the Young graph.. Algebraic Combinatorics, 5 (3), 413-436. https://arxiv.org/abs/2009.08181
Koirala, S., Jones, C., Ahrens, B., Fan, N., Brovkin, V., Delire, C., Fan, Y., Gayler, V., Joetzjer, E., Lee, H., Materia, S., Nabel, J., Peano, D., Peylin, P., Wårlind, D., Wiltshire, A., Zaehle, S., Reichstein, M., & Carvalhais, N. (2022). Underrepresented controls of aridity in climate sensitivity of carbon cycle models. NA. https://hal.science/hal-04302319/
Kondylatos, S., Prapas, I., Ronco, M., Papoutsis, I., Camps‐Valls, G., Piles, M., Fernández‐Torres, M., & Carvalhais, N. (2022). Wildfire danger prediction and understanding with Deep Learning. Geophysical Research Letters, 49 (17), e2022GL099368. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2022GL099368
August (6)
Reichstein, M., Ahrens, B., Kraft, B., Camps-Valls, G., Carvalhais, N., Gans, F., Gentine, P., & Winkler, A. J. (2022). Combining system modeling and machine learning into hybrid ecosystem modeling. NA, 327-352. https://www.taylorfrancis.com/chapters/edit/10.1201/9781003143376-14/combining-system-modeling-machine-learning-hybrid-ecosystem-modeling-markus-reichstein-bernhard-ahrens-basil-kraft-gustau-camps-valls-nuno-carvalhais-fabian-gans-pierre-gentine-alexander-winkler
Luo, Y., Pacheco-Labrador, J., Richardson, A. D., Seyednasrollah, B., Perez-Priego, O., Gonzalez-Cascon, R., Martín, M. P., Moreno, G., Nair, R., Wutzler, T., Bucher, S. F., Carrara, A., Cremonese, E., El-Madany, T. S., Filippa, G., Galvagno, M., Hammer, T., Ma, X., Martini, D., Zhang, Q., Reichstein, M., Menzel, A., Römermann, C., & Migliavacca, M. (2022). Evergreen broadleaf greenness and its relationship with leaf flushing, aging, and water fluxes. Agricultural and Forest Meteorology, 323, 109060. https://www.sciencedirect.com/science/article/pii/S0168192322002490
Körschens, M., Bodesheim, P., & Denzler, J. (2022). Occlusion-robustness of convolutional neural networks via inverted cutout. NA, 2829-2835. https://ieeexplore.ieee.org/abstract/document/9956044/
Wang, S., Yang, H., Koirala, S., Forkel, M., Reichstein, M., & Carvalhais, N. (2022). Understanding disturbance regimes from patterns in biomass and primary productivity. NA. https://essopenarchive.org/doi/full/10.1002/essoar.10512199.1
Eulzer, P., Meuschke, M., Mistelbauer, G., & Lawonn, K. (2022). Vessel maps: A Survey of map‐like visualizations of the cardiovascular system. Computer graphics forum, 41 (3), 645-673. https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.14576
Wahl, J. (2022). Where are all the guns? Modeling firearm ownership in the United States. Patterns, 3 (8). https://www.cell.com/patterns/fulltext/S2666-3899(22)00182-9
July (5)
Ahmad, W., Shadaydeh, M., & Denzler, J. (2022). Causal discovery using model invariance through knockoff interventions. NA. https://arxiv.org/abs/2207.04055
Rewicki, F., & Gawlikowski, J. (2022). Estimating Uncertainty of Deep Learning Multi-Label Classifications Using Laplace Approximation. NA, 1560-1563. https://ieeexplore.ieee.org/abstract/document/9884167/
Schneider, M., & Körner, M. (2022). Harnessing Administrative Data Inventories to Create a Reliable Transnational Reference Database for Crop Type Monitoring. NA, 5385-5388. https://ieeexplore.ieee.org/abstract/document/9883089/
Behrens, G., Beucler, T., Gentine, P., Iglesias‐Suarez, F., Pritchard, M., & Eyring, V. (2022). Non‐linear dimensionality reduction with a variational encoder decoder to understand convective processes in climate models. Journal of Advances in Modeling Earth Systems, 14 (8), e2022MS003130. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2022MS003130
Gawlikowski, J., Saha, S., Niebling, J., & Zhu, X. X. (2022). Robust Distribution-Shift Aware Sar-Optical data Fusion for Multi-Label Scene Classification. NA, 911-914. https://ieeexplore.ieee.org/abstract/document/9884880/
June (10)
Knevels, R., Petschko, H., Proske, H., Leopold, P., Mishra, A. N., Maraun, D., & Brenning, A. (2022). Assessing uncertainties in landslide susceptibility predictions in a changing environment (Styrian Basin, Austria). Natural Hazards and Earth System Sciences Discussions, 2022, 1-42. https://nhess.copernicus.org/articles/23/205/2023/
Lieb, S. J., Klee, N., & Lawonn, K. (2022). Clasping Trees-A Pipeline for Interactive Procedural Tree Generation.. NA, 49-56. https://diglib.eg.org/bitstreams/5cc6e5a1-6baa-47e7-a283-3a0ebc613faf/download
Knevels, R., Petschko, H., Proske, H., Leopold, P., Mishra, A. N., Maraun, D., & Brenning, A. (2022). Future storylines of landslide susceptibility in the Styrian Basin, Austria. Accounting for environmental change and uncertainties. NA, NA (ICG2022-716). https://meetingorganizer.copernicus.org/ICG2022/ICG2022-716.html
Friedrich, H., Tellman, B., Mukherjee, R., Lakshmi, V. V., Lall, U., Kruczkiewicz, A., & Gentine, P. (2022). Model comparison to evaluate the added value of commercial high-resolution satellite imagery for urban flood detection. Frontiers in Hydrology 2022, 104-02. https://ui.adsabs.harvard.edu/abs/2022frhy.conf10402F/abstract
Laue, S., Blacher, M., & Giesen, J. (2022). Optimization for Classical Machine Learning Problems on the GPU. Proceedings of the AAAI Conference on Artificial Intelligence, 36 (7), 7300-7308. https://ojs.aaai.org/index.php/AAAI/article/view/20692
Shadaydeh, M., Denzler, J., & Migliavacca, M. (2022). Partitioning of Net Ecosystem Exchange Using Dynamic Mode Decomposition and Time Delay Embedding. Engineering Proceedings, 18 (1), 13. https://www.mdpi.com/2673-4591/18/1/13
Bodesheim, P., Babst, F., Frank, D. C., Hartl, C., Zang, C. S., Jung, M., Reichstein, M., & Mahecha, a. M. D. (2022). Predicting spatiotemporal variability in radial tree growth at the continental scale with machine learning. Environmental Data Science, 1, 10.1017/eds.2022.8. https://www.cambridge.org/core/journals/environmental-data-science/article/predicting-spatiotemporal-variability-in-radial-tree-growth-at-the-continental-scale-with-machine-learning/560F105E7274274411DA0308C1EC144F
Wang, Z., Goetz, J., & Brenning, A. (2022). Predicting the unknown: using transfer learning techniques for landslide susceptibility modelling. NA, NA (ICG2022-718). https://meetingorganizer.copernicus.org/ICG2022/ICG2022-718.html
Theiß, C., & Denzler, J. (2022). Towards a Unified Benchmark for Monocular Radial Distortion Correction and the Importance of Testing on Real-World Data. NA, 59-71. https://link.springer.com/chapter/10.1007/978-3-031-09037-0_6
Zhan, W., Yang, X., Ryu, Y., Dechant, B., Huang, Y., Goulas, Y., Kang, M., & Gentine, P. (2022). Two for one: Partitioning CO2 fluxes and understanding the relationship between solar-induced chlorophyll fluorescence and gross primary productivity using machine learning. Agricultural and Forest Meteorology, 321, 108980. https://www.sciencedirect.com/science/article/pii/S0168192322001708
May (7)
Meuschke, M., Voß, S., Eulzer, P., Janiga, G., Arens, C., Wickenhöfer, R., Preim, B., & Lawonn, K. (2022). COMFIS-Comparative Visualization of Simulated Medical Flow Data.. NA, 29-40. https://www.researchgate.net/profile/Monique-Meuschke/publication/365802008_COMFIS_-_Comparative_Visualization_of_Simulated_Medical_Flow_Data/links/6384e5f47b0e356feb92d80e/COMFIS-Comparative-Visualization-of-Simulated-Medical-Flow-Data.pdf
Camps-Valls, G. (2022). Commentary on ‘Physics-informed deep learning parameterization of ocean vertical mixing improves climate simulations’ by Zhu et al.. National Science Review, 9 (8), nwac092. https://academic.oup.com/nsr/article-abstract/9/8/nwac092/6588059
Papanikolaou, N., Vaccario, G., Hormann, E., Lambiotte, R., & Schweitzer, F. (2022). Consensus from group interactions: An adaptive voter model on hypergraphs. NA, 105 (5), 054307. https://journals.aps.org/pre/abstract/10.1103/PhysRevE.105.054307
Cheng, Y., Giometto, M. G., Kauffmann, P., Lin, L., Cao, C., Zupnick, C., Li, H., Li, Q., Huang, Y., Abernathey, R., & Gentine, P. (2022). Deep learning for subgrid‐scale turbulence modeling in large‐eddy simulations of the convective atmospheric boundary layer. Journal of Advances in Modeling Earth Systems, 14 (5), e2021MS002847. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2021MS002847
Brigato, L., Barz, B., Iocchi, L., & Denzler, J. (2022). Image classification with small datasets: Overview and benchmark. NA, 10, 49233-49250. https://ieeexplore.ieee.org/abstract/document/9770050/
Cortés-Andrés, J., Camps-Valls, G., Sippel, S., Székely, E., Sejdinovic, D., Diaz, E., Pérez-Suay, A., Li, Z., Mahecha, M., & Reichstein, M. (2022). Physics-aware nonparametric regression models for Earth data analysis. Environmental Research Letters, 17 (5), 054034. https://iopscience.iop.org/article/10.1088/1748-9326/ac6762/meta
Wang, Z., Goetz, J., & Brenning, A. (2022). Transfer learning for landslide susceptibility modelling using domain adaptation and case-based reasoning. Geoscientific Model Development Discussions, 2022, 1-30. https://gmd.copernicus.org/articles/15/8765/2022/
April (5)
Eulzer, P., Rockenfeller, R., & Lawonn, K. (2022). HAExplorer: Understanding Interdependent Biomechanical Motions with Interactive Helical Axes. NA, 1-16. https://dl.acm.org/doi/abs/10.1145/3491102.3501841
Hombeck, J., Lichtenberg, N., & Lawonn, K. (2022). Heads up a study of assistive visualizations for localisation guidance in virtual reality. NA, 83-88. https://link.springer.com/chapter/10.1007/978-3-658-36932-3_18
Zhou, S., Keenan, T. F., Williams, A. P., Lintner, B. R., Zhang, Y., & Gentine, P. (2022). Large Divergence in Tropical Hydrological Projections Caused by Model Spread in Vegetation Responses to Elevated CO2. Earth’s Future, 10 (4), e2021EF002457. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2021EF002457
Salcedo-Sanz, S., Casillas-Pérez, D., Ser, J. D., Casanova-Mateo, C., Cuadra, L., Piles, M., & Camps-Valls, G. (2022). Persistence in complex systems. NA, 957, 1-73. https://www.sciencedirect.com/science/article/pii/S0370157322000497
Bodesheim, P., Blunk, J., Koerschens, M., Brust, C., Kaeding, C., & Denzler, J. (2022). Pre-trained models are not enough: active and lifelong learning is important for long-term visual monitoring of mammals in biodiversity research—individual identification and …. Mammalian Biology, 102 (3), 875-897. https://link.springer.com/article/10.1007/s42991-022-00224-8
March (13)
Camps-Valls, G., Tuia, D., Gómez-Chova, L., Jiménez, S., & Malo, J. (2022). Classification of Remote Sensing Images. NA, 78-111. https://link.springer.com/chapter/10.1007/978-3-031-02247-0_4
Zhou, K., Zhang, Q., Xiong, L., & Gentine, P. (2022). Estimating evapotranspiration using remotely sensed solar-induced fluorescence measurements. Agricultural and Forest Meteorology, 314, 108800. https://www.sciencedirect.com/science/article/pii/S016819232100486X
Camps-Valls, G., Tuia, D., Gómez-Chova, L., Jiménez, S., & Malo, J. (2022). Estimation of Physical Parameters. NA, 136-165. https://link.springer.com/chapter/10.1007/978-3-031-02247-0_6
Hombeck, J., Meuschke, M., Zyla, L., Heuser, A., Toader, J., Popp, F., Bruns, C. J., Hansen, C., Datta, R. R., & Lawonn, K. (2022). Evaluating perceptional tasks for medicine: A comparative user study between a virtual reality and a desktop application. NA, 514-523. https://ieeexplore.ieee.org/abstract/document/9756759/
Martini, D., Sakowska, K., Wohlfahrt, G., Pacheco‐Labrador, J., Tol, C. V. d., Porcar‐Castell, A., Magney, T. S., Carrara, A., Colombo, R., El‐Madany, T. S., Gonzalez‐Cascon, R., Martín, M. P., Julitta, T., Moreno, G., Rascher, U., Reichstein, M., Rossini, M., & Migliavacca, M. (2022). Heatwave breaks down the linearity between sun‐induced fluorescence and gross primary production. New Phytologist, 233 (6), 2415-2428. https://nph.onlinelibrary.wiley.com/doi/abs/10.1111/nph.17920
Fan, N., Reichstein, M., Koirala, S., Mahecha, M., Ahrens, B., & Carvalhais, N. (2022). Hydrometeorology influences the apparent temperature sensitivity of terrestrial carbon turnover times. NA. https://pure.mpg.de/rest/items/item_3372839/component/file_3372840/content
An, S., Chen, X., Shen, M., Zhang, X., Lang, W., & Liu, G. (2022). Increasing interspecific difference of alpine herb phenology on the eastern Qinghai-Tibet Plateau. Frontiers in Plant Science, 13, 844971. https://www.frontiersin.org/articles/10.3389/fpls.2022.844971/full
Camps-Valls, G., Tuia, D., Gómez-Chova, L., Jiménez, S., & Malo, J. (2022). Remote Sensing Feature Selection and Extraction. NA, 48-77. https://link.springer.com/chapter/10.1007/978-3-031-02247-0_3
Camps-Valls, G., Tuia, D., Gómez-Chova, L., Jiménez, S., & Malo, J. (2022). Remote sensing from earth observation satellites. NA, 1-25. https://link.springer.com/chapter/10.1007/978-3-031-02247-0_1
Camps-Valls, G., Tuia, D., Gómez-Chova, L., Jiménez, S., & Malo, J. (2022). Spectral Mixture Analysis. NA, 112-135. https://link.springer.com/chapter/10.1007/978-3-031-02247-0_5
Camps-Valls, G., Tuia, D., Gómez-Chova, L., Jiménez, S., & Malo, J. (2022). The Statistics of Remote Sensing Images. NA, 26-47. https://link.springer.com/chapter/10.1007/978-3-031-02247-0_2
Kraft, B., Jung, M., Körner, M., Koirala, S., & Reichstein, M. (2022). Towards hybrid modeling of the global hydrological cycle. Hydrology and Earth System Sciences, 26 (6), 1579-1614. https://hess.copernicus.org/articles/26/1579/2022/
Wahl, J. (2022). Traces on diagram algebras I: Free partition quantum groups, random lattice paths and random walks on trees. Journal of the London Mathematical Society, 105 (4), 2324-2372. https://londmathsoc.onlinelibrary.wiley.com/doi/abs/10.1112/jlms.12562
February (0)
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January (11)
Gawlikowski, J., Saha, S., Kruspe, A., & Zhu, X. X. (2022). An advanced dirichlet prior network for out-of-distribution detection in remote sensing. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-19. https://ieeexplore.ieee.org/abstract/document/9668955/
Hissbach, A., Dick, C., & Lawonn, K. (2022). An Overview of Techniques for Egocentric Black Hole Visualization and Their Suitability for Planetarium Applications, 83-90. https://diglib.eg.org/bitstream/handle/10.2312/vmv20221207/083-090.pdf
Körschens, M., Bodesheim, P., & Denzler, J. (2022). Beyond Global Average Pooling: Alternative Feature Aggregations for Weakly Supervised Localization.. NA, 180-191. https://www.researchgate.net/profile/Matthias-Koerschens/publication/358596366_Beyond_Global_Average_Pooling_Alternative_Feature_Aggregations_for_Weakly_Supervised_Localization/links/620e5a51eb735c508adb1b76/Beyond-Global-Average-Pooling-Alternative-Feature-Aggregations-for-Weakly-Supervised-Localization.pdf
Joswig, J. S., Wirth, C., Schuman, M. C., Kattge, J., Reu, B., Wright, I. J., Sippel, S. D., Rüger, N., Richter, R., Schaepman, M. E., Bodegom, P. M. v., Cornelissen, J. H., Díaz, S., Hattingh, W. N., Kramer, K., Lens, F., Niinemets, Ü., Reich, P. B., Reichstein, M., Römermann, C., Schrodt, F., Anand, M., Bahn, M., Byun, C., Campetella, G., Cerabolini, B. E., Craine, J. M., Gonzalez-Melo, A., Gutiérrez, A. G., He, T., Higuchi, P., Jactel, H., Kraft, N. J., Minden, V., Onipchenko, V., Peñuelas, J., Pillar, V. D., Sosinski, Ê., Soudzilovskaia, N. A., Weiher, E., & Mahecha, M. D. (2022). Climatic and soil factors explain the two-dimensional spectrum of global plant trait variation. Nature ecology & evolution, 6 (1), 36-50. https://www.nature.com/articles/s41559-021-01616-8
Reimers, C., Bodesheim, P., & Runge, J. (2022). Conditional Adversarial Debiasing: Towards Learning Unbiased Classifiers. Pattern Recognition: 43rd DAGM German Conference, DAGM GCPR 2021, Bonn, Germany, September 28–October 1, 2021, Proceedings, 13024, 48. https://books.google.com/books?hl=en&lr=&id=zDZZEAAAQBAJ&oi=fnd&pg=PA48&dq=info:lqjPp2QIaiUJ:scholar.google.com&ots=a-XvGu4Hb8&sig=XumNVGjrThQfvAe3VSAlLnFNKG4
Orth, R., O, S., Zscheischler, J., Mahecha, M. D., & Reichstein, M. (2022). Contrasting biophysical and societal impacts of hydro-meteorological extremes. Environmental Research Letters, 17 (1), 014044. https://iopscience.iop.org/article/10.1088/1748-9326/ac4139/meta
Persello, C., Wegner, J. D., Hänsch, R., Tuia, D., Ghamisi, P., Koeva, M., & Camps-Valls, G. (2022). Deep learning and earth observation to support the sustainable development goals: Current approaches, open challenges, and future opportunities. NA, 10 (2), 172-200. https://ieeexplore.ieee.org/abstract/document/9681713/
Bodesheim, D. K. P., & Denzler, J. (2022). End-to-End Learning of Fisher Vector. Pattern Recognition: 43rd DAGM German Conference, DAGM GCPR 2021, Bonn, Germany, September 28–October 1, 2021, Proceedings, 13024, 142. https://books.google.com/books?hl=en&lr=&id=zDZZEAAAQBAJ&oi=fnd&pg=PA142&dq=info:2uX84Z_RIVQJ:scholar.google.com&ots=a-XvGx8C5c&sig=zgryutxPWbpKR1_8f7dubsiFZMU
Bao, S., Wutzler, T., Koirala, S., Cuntz, M., Ibrom, A., Besnard, S., Walther, S., Šigut, L., Moreno, A., Weber, U., Wohlfahrt, G., Cleverly, J., Migliavacca, M., Woodgate, W., Merbold, L., Veenendaal, E., & Carvalhais, N. (2022). Environment-sensitivity functions for gross primary productivity in light use efficiency models. Agricultural and Forest Meteorology, 312, 108708. https://www.sciencedirect.com/science/article/pii/S0168192321003944
Díaz, E., Adsuara, J. E., Martínez, Á. M., Piles, M., & Camps-Valls, G. (2022). Inferring causal relations from observational long-term carbon and water fluxes records. Scientific Reports, 12 (1), 1610. https://www.nature.com/articles/s41598-022-05377-7
Jiang, S., Zheng, Y., Wang, C., & Babovic, V. (2022). Uncovering flooding mechanisms across the contiguous United States through interpretive deep learning on representative catchments. Water Resources Research, 58 (1), e2021WR030185. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2021WR030185