Christian Requena-Mesa

Most-cited papers

1. Global COVID-19 lockdown highlights humans as both threats and custodians of the environment

   A. Bates, R. Primack, Brandy S. Biggar, Tomas Bird +342 more

   2021Biological Conservation148 citations Open access

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2. EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task

   Christian Requena-Mesa, V. Benson, M. Reichstein, J. Runge +1 more

   20212021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)82 citations Open access

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3. Identifying Dynamic Memory Effects on Vegetation State Using Recurrent Neural Networks

   Basil Kraft, M. Jung, Marco Körner, Christian Requena-Mesa +2 more

   2019Frontiers in Big Data55 citations Open access

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4. Multi-Modal Learning for Geospatial Vegetation Forecasting

   V. Benson, Claire Robin, Christian Requena-Mesa, Lazaro Alonso +6 more

   2023Computer Vision and Pattern Recognition42 citations Open access

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5. Generating Physically-Consistent Satellite Imagery for Climate Visualizations

   Bjorn Lutjens, Brandon Leshchinskiy, O. Boulais, F. Chishtie +10 more

   2021IEEE Transactions on Geoscience and Remote Sensing28 citations Open access

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6. Physics-informed GANs for Coastal Flood Visualization

   Björn Lütjens, Brandon Leshchinskiy, Christian Requena-Mesa, F. Chishtie +7 more

   2020arXiv.org20 citations

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7. Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMs

   Claire Robin, Christian Requena-Mesa, V. Benson, Lazaro Alonso +3 more

   2022arXiv.org17 citations Open access

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8. Integration of a Deep‐Learning‐Based Fire Model Into a Global Land Surface Model

   Rackhun Son, T. Stacke, V. Gayler, J. Nabel +8 more

   2024Journal of Advances in Modeling Earth Systems16 citations Open access

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9. Spatio‐temporal Autoencoders in Weather and Climate Research

   Xavier-Andoni Tibau, Christian Reimers, Christian Requena-Mesa, J. Runge

   2021Deep Learning for the Earth Sciences16 citations

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10. Deep Learning – an Opportunity and a Challenge for Geo- and Astrophysics

    Christian Reimers, Christian Requena-Mesa

    202013 citations

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11. Predicting Landscapes from Environmental Conditions Using Generative Networks

    Christian Requena-Mesa, M. Reichstein, M. Mahecha, Basil Kraft +1 more

    2019German Conference on Pattern Recognition13 citations Open access

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12. Predicting Landscapes as Seen from Space from Environmental Conditions

    Christian Requena-Mesa, M. Reichstein, M. Mahecha, Basil Kraft +1 more

    2018IEEE International Geoscience and Remote Sensing Symposium12 citations

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13. SupernoVAE : VAE based kernel PCA for analysis of spatio-temporal Earth data

    Xavier-Andoni Tibau, Christian Requena-Mesa, Christian Reimers, Joachim Denzler +3 more

    201810 citations

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14. Physically-Consistent Generative Adversarial Networks for Coastal Flood Visualization

    Bjorn Lutjens, Brandon Leshchinskiy, Christian Requena-Mesa, F. Chishtie +8 more

    2021arXiv.org8 citations

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15. EarthNet2021: A novel large-scale dataset and challenge for forecasting localized climate impacts

    Christian Requena-Mesa, V. Benson, Joachim Denzler, Jakob Runge +1 more

    2020arXiv.org5 citations

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16. Generative Adversarial Networks in the Geosciences

    Gonzalo Mateo-García, V. Laparra, Christian Requena-Mesa, L. Gómez-Chova

    2021Deep Learning for the Earth Sciences3 citations

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17. Integration of a deep-learning-based fire model into a global land surface model

    Rackhun Son, T. Stacke, V. Gayler, J. Esther +11 more

    1 citations

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18. List of Contributors

    T. Al-Ubaidi, I. Andronov, V. Andruk, I. Babyk +49 more

    2020Knowledge Discovery in Big Data from Astronomy and Earth Observation0 citations

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19. SupernoVAE: Using deep learning to find spatio-temporal dynamics in Earth system data

    Christian Reimers, X. A. Tibau Alberdi, Christian Requena-Mesa, Joachim Denzler +3 more

    20180 citations

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