Publikationen von Christian Requena Mesa

Lütjens Björn, Brandon Leshchinskiy, Océane Boulais, Farrukh Chishtie, Natalia Díaz-Rodríguez, Margaux Masson-Forsythe, Ana Mata-Payerro, Christian Requena Mesa, Aruna Sankaranarayanan, A. Piña, Y. Gal, C. Raïssi, A. Lavin, and D. Newman, "Generating physically-consistent satellite imagery for climate visualizations," IEEE Transactions on Geoscience and Remote Sensing 62, 4213311 (2024).

Rackhun Son, Tobias Stacke, Veronika Gayler, Julia E. M. S. Nabel, Reiner Schnur, Lazaro Alonso, Christian Requena Mesa, Alexander Winkler, Stijn Hantson, Sönke Zaehle, Ulrich Weber, and Nuno Carvalhais, "Integration of a deep-learning-based fire model into a global land surface model," Journal of Advances in Modeling Earth Systems 16 (1), e2023MS003710 (2024).

Amanda E. Bates, Richard B. Primack, Brandy S. Biggar, Tomas J. Bird, Mary E. Clinton, Rylan J. Command, Cerren Richards, Marc Shellard, Nathan R. Geraldi, Valeria Vergara, Orlando Acevedo-Charry, Christian Requena Mesa, and al. et, "Global COVID-19 lockdown highlights humans as both threats and custodians of the environment," Biological Conservation 263, 109175 (2021).

Basil Kraft, Martin Jung, Marco Körner, Christian Requena Mesa, José Cortés, and Markus Reichstein, "Identifying dynamic memory effects on vegetation state using recurrent neural networks," Frontiers in Big Data 2, 31 (2019).

Gonzalo Mateo-García, Valero Laparra, Christian Requena Mesa, and Luis Gómez-Chova, "Generative adversarial networks in the Geosciences", in Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science, and Geosciences, edited by Gustau Camps-Valls, Devis Tuia, Xiao Xiang Zhu, and Markus Reichstein (John Wiley & Sons Ltd, Hoboken, New Jersey, 2021), pp. 24-36.

Xavier-Andoni Tibau, Christian Reimers, Christian Requena Mesa, and Jakob Runge, "Spatio-temporal Autoencoders in Weather and Climate Research", in Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science, and Geosciences, edited by Gustau Camps-Valls, Devis Tuia, Xiao Xiang Zhu, and Markus Reichstein (John Wiley & Sons Ltd, Hoboken, New Jersey, 2021), pp. 186-203.

Christian Requena-Mesa, Markus Reichstein, Miguel D. Mahecha, Basil Kraft, and Joachim Denzler, "Predicting landscapes from environmental conditions using generative networks", in Pattern Recognition, DAGM GCPR 2019, edited by G. A. FInk, S. Frintrop, and X. Jiang (Springer, Cham, 2019), pp. 203-217.

Vitus Benson, Claire Robin, Christian Requena Mesa, Lazaro Alonso, Nuno Carvalhais, José Cortés, Zhihan Gao, Nora Linscheid, Melanie Weynants, and Markus Reichstein, "Multi-modal learning for geospatial vegetation forecasting", in 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2024).

Christian Requena Mesa, Vitus Benson, Markus Reichstein, Jakob Runge, and Joachim Denzler, "EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task", in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), (IEEE, Nashville, TN, USA, 2021).

Christian Requena-Mesa, Markus Reichstein, Miguel D. Mahecha, Basil Kraft, and J. Denzler, "Predicting landscapes as seen from space from environmental conditions", in 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), (2018), pp. 1768-1771.

Vitus Benson, Christian Requena Mesa, Claire Robin, Lazaro Alonso, José Cortés, Zhihan Gao, Nora Linscheid, Melanie Weynants, and Markus Reichstein, "Forecasting localized weather impacts on vegetation as seen from space with meteo-guided video prediction", in arXiv, (2023).

Claire Robin, Christian Requena Mesa, Vitus Benson, Lazaro Alonso, Jeran Poehls, Nuno Carvalhais, and Markus Reichstein, "Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMs", in arXiv, (2022).