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Predicting landscapes from environmental conditions using generative networks

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Requena-Mesa,  Christian
Empirical Inference of the Earth System, Dr. Miguel D. Mahecha, Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;
IMPRS International Max Planck Research School for Global Biogeochemical Cycles, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Reichstein,  Markus
Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Mahecha,  Miguel D.
Empirical Inference of the Earth System, Dr. Miguel D. Mahecha, Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Kraft,  Basil
Global Diagnostic Modelling, Dr. Martin Jung, Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;
IMPRS International Max Planck Research School for Global Biogeochemical Cycles, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Citation

Requena-Mesa, C., Reichstein, M., Mahecha, M. D., Kraft, B., & Denzler, J. (2019). Predicting landscapes from environmental conditions using generative networks. In G. A. FInk, S. Frintrop, & X. Jiang (Eds.), Pattern Recognition, DAGM GCPR 2019 (pp. 203-217). Cham: Springer. doi:10.1007/978-3-030-33676-9_14.


Cite as: https://hdl.handle.net/21.11116/0000-0005-B21D-2
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