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Nuno Carvalhais
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- affiliation: Max Planck Institute for Biogeochemistry, Jena, Germany
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2020 – today
- 2024
- [c5]Vitus Benson, Claire Robin, Christian Requena-Mesa, Lázaro Alonso, Nuno Carvalhais, José Cortés, Zhihan Gao, Nora Linscheid, Mélanie Weynants, Markus Reichstein:
Multi-Modal Learning for Geospatial Vegetation Forecasting. CVPR 2024: 27788-27799 - [i5]Kai-Hendrik Cohrs, Gherardo Varando, Nuno Carvalhais, Markus Reichstein, Gustau Camps-Valls:
Double machine learning for causal hybrid modeling - applications in the Earth sciences. CoRR abs/2402.13332 (2024) - 2023
- [c4]Henrik Voigt, Nuno Carvalhais, Monique Meuschke, Markus Reichstein, Sina Zarrieß, Kai Lawonn:
VIST5: An Adaptive, Retrieval-Augmented Language Model for Visualization-oriented Dialog. EMNLP (Demos) 2023: 70-81 - [i4]Ilektra Karasante, Lázaro Alonso, Ioannis Prapas, Akanksha Ahuja, Nuno Carvalhais, Ioannis Papoutsis:
SeasFire as a Multivariate Earth System Datacube for Wildfire Dynamics. CoRR abs/2312.07199 (2023) - 2022
- [j5]Çaglar Küçük
, Sujan Koirala, Nuno Carvalhais, Diego G. Miralles, Markus Reichstein
Observation-based assessment of secondary water effects on seasonal vegetation decay across Africa. Frontiers Big Data 5 (2022) - [i3]Claire Robin, Christian Requena-Mesa, Vitus Benson, Lázaro Alonso, Jeran Poehls
Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMs. CoRR abs/2210.13648 (2022) - [i2]Ioannis Prapas, Akanksha Ahuja, Spyros Kondylatos
Deep Learning for Global Wildfire Forecasting. CoRR abs/2211.00534 (2022) - 2021
- [i1]Ioannis Prapas, Spyros Kondylatos, Ioannis Papoutsis, Gustau Camps-Valls, Michele Ronco, Miguel-Ángel Fernández-Torres, Maria Piles Guillem, Nuno Carvalhais:
Deep Learning Methods for Daily Wildfire Danger Forecasting. CoRR abs/2111.02736 (2021)
2010 – 2019
- 2019
- [j4]Pedro Rodríguez-Veiga
Forest biomass retrieval approaches from earth observation in different biomes. Int. J. Appl. Earth Obs. Geoinformation 77: 53-68 (2019) - [j3]Markus Reichstein
Deep learning and process understanding for data-driven Earth system science. Nat. 566(7743): 195-204 (2019) - 2018
- [c3]Javier Pacheco-Labrador
Assessing the Use of Multiple Constraints and Ancillary Data to Support Scope Model Inversion in a Experimental Grassland. IGARSS 2018: 5975-5978 - [c2]Markus Reichstein
Modelling Landsurface Time-Series with Recurrent Neural Nets. IGARSS 2018: 7640-7643 - 2014
- [j2]Christopher S. R. Neigh
An Automated Approach to Map the History of Forest Disturbance from Insect Mortality and Harvest with Landsat Time-Series Data. Remote. Sens. 6(4): 2782-2808 (2014) - 2013
- [j1]Matthias Forkel
Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology. Remote. Sens. 5(5): 2113-2144 (2013)
2000 – 2009
- 2003
- [c1]Paulo Lopes, Pedro Lourenço, Nuno Carvalhais, Júlia Seixas
MODIS land cover product validation in the Iberian Peninsula. IGARSS 2003: 1733-1735
Coauthor Index
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