28 citations · 59 across the 7 of their papers we have counts for
4 papers · 1 filter
Transformers vs. Recurrent Models for Estimating Forest Gross Primary Production
David Montero, Miguel D. Mahecha, Francesco Martinuzzi +6
Monitoring the spatiotemporal dynamics of forest CO uptake (Gross Primary Production, GPP), remains a central challenge in terrestrial ecosystem research. While Eddy Covariance…
Unified Implementations of Recurrent Neural Networks in Multiple Deep Learning Frameworks
Francesco Martinuzzi
Recurrent neural networks (RNNs) are a cornerstone of sequence modeling across various scientific and industrial applications. Owing to their versatility, numerous RNN variants hav…
DeepExtremeCubes: Integrating Earth system spatio-temporal data for impact assessment of climate extremes
Chaonan Ji, Tonio Fincke, Vitus Benson +12
With climate extremes' rising frequency and intensity, robust analytical tools are crucial to predict their impacts on terrestrial ecosystems. Machine learning techniques show prom…
Recurrent Neural Networks for Modelling Gross Primary Production
David Montero, Miguel D. Mahecha, Francesco Martinuzzi +6
Accurate quantification of Gross Primary Production (GPP) is crucial for understanding terrestrial carbon dynamics. It represents the largest atmosphere-to-land CO flux, especi…