| Global COVID-19 lockdown highlights humans as both threats and custodians of the environment AE Bates, RB Primack, BS Biggar, TJ Bird, ME Clinton, RJ Command, ... Biological conservation 263, 109175, 2021 | 110 | 2021 |
| EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task. C Requena-Mesa, V Benson, M Reichstein, J Runge, J Denzler Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 21 | 2021 |
| Identifying dynamic memory effects on vegetation state using recurrent neural networks B Kraft, M Jung, M Körner, C Requena Mesa, J Cortés, M Reichstein Frontiers in big Data 2, 31, 2019 | 19 | 2019 |
| Deep learning–an opportunity and a challenge for geo-and astrophysics C Reimers, C Requena-Mesa Knowledge discovery in big data from astronomy and earth observation, 251-265, 2020 | 17 | 2020 |
| Physics-informed GANs for coastal flood visualization B Lütjens, B Leshchinskiy, C Requena-Mesa, F Chishtie, ... arXiv preprint arXiv:2010.08103, 2020 | 14 | 2020 |
| Predicting landscapes from environmental conditions using generative networks C Requena-Mesa, M Reichstein, M Mahecha, B Kraft, J Denzler German Conference on Pattern Recognition, 203-217, 2019 | 11 | 2019 |
| Physically-consistent generative adversarial networks for coastal flood visualization B Lütjens, B Leshchinskiy, C Requena-Mesa, F Chishtie, ... arXiv preprint arXiv:2104.04785, 2021 | 10 | 2021 |
| Predicting landscapes as seen from space from environmental conditions C Requena-Mesa, M Reichstein, M Mahecha, B Kraft, J Denzler IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium …, 2018 | 7 | 2018 |
| Identifying Dynamic Memory Effects on Vegetation State Using Recurrent Neural Networks, Front. Big Data, 2, 31 B Kraft, M Jung, M Körner, C Requena Mesa, J Cortés, M Reichstein | 5 | 2019 |
| Spatio‐temporal Autoencoders in Weather and Climate Research XA Tibau, C Reimers, C Requena‐Mesa, J Runge Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote …, 2021 | 4 | 2021 |
| Supernovae: VAE Based Kernel-PCA For Analysis of Spatio-Temporal Earth Data XA Tibau, C Requena-Mesa, C Reimers, J Denzler, V Eyring, ... Climate Informatics Workshop 2018, 2018 | 3 | 2018 |
| EarthNet2021: A novel large-scale dataset and challenge for forecasting localized climate impacts C Requena-Mesa, V Benson, J Denzler, J Runge, M Reichstein arXiv preprint arXiv:2012.06246, 2020 | 2 | 2020 |
| Integration of a deep-learning-based fire model into a global land surface model R Son, T Stacke, V Gayler, JEMS Nabel, R Schnur, LA Silva, CR Mesa, ... Authorea Preprints, 2023 | 1 | 2023 |
| Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMs C Robin, C Requena-Mesa, V Benson, L Alonso, J Poehls, N Carvalhais, ... arXiv preprint arXiv:2210.13648, 2022 | 1 | 2022 |
| DeepExtremes: Explainable Earth Surface Forecasting Under Extreme Climate Conditions K Mora, G Brandt, V Benson, C Brockmann, G Camps-Valls, ... EGU General Assembly Conference Abstracts, EGU-12657, 2023 | | 2023 |
| Forecasting localized weather impacts on vegetation as seen from space with meteo-guided video prediction V Benson, C Requena-Mesa, C Robin, L Alonso, J Cortés, Z Gao, ... arXiv preprint arXiv:2303.16198, 2023 | | 2023 |
| Modeling landscape-scale vegetation response to climate: Synthesis of the EarthNet challenge V Benson, C Requena-Mesa, C Robin, L Alonso, N Carvalhais, ... EGU23, 2023 | | 2023 |
| Modeling vegetation response to climate in Africa at fine resolution: EarthNet2023, a deep learning dataset and challenge. C Robin, C Requena-Mesa, V Benson, L Alonso, J Poehls, N Carvalhais, ... EGU23, 2023 | | 2023 |
| Generative Adversarial Networks in the Geosciences G Mateo‐García, V Laparra, C Requena‐Mesa, L Gómez‐Chova Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote …, 2021 | | 2021 |
| EarthNet2021: Self-supervised impact predictions of extreme weather. C Requena Mesa, V Benson, J Denzler, J Runge, M Reichstein EGU General Assembly Conference Abstracts, EGU21-1612, 2021 | | 2021 |
Markus ReichsteinDepartment of Biogeochemical Integration, Max-Planck-Institute for BiogeochemistryDirección de correo verificada de bgc-jena.mpg.de
