28 citations · 50 across the 8 of their papers we have counts for
8 papers
Context-Aware Multimodal Representation Learning for Spatio-Temporally Explicit Environmental Modelling
Julia Peters, Karin Mora, Miguel D. Mahecha +4
Earth observation (EO) foundation models have emerged as an effective approach to derive latent representations of the Earth system from various remote sensing sensors. These model…
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…
AI for a Planet Under Pressure
Victor Galaz, Maria Schewenius, Jonathan F. Donges +26
Artificial intelligence (AI) is already driving scientific breakthroughs in a variety of research fields, ranging from the life sciences to mathematics. This raises a critical ques…
Earth System Data Cubes: Avenues for advancing Earth system research
David Montero, Guido Kraemer, Anca Anghelea +15
Recent advancements in Earth system science have been marked by the exponential increase in the availability of diverse, multivariate datasets characterised by moderate to high spa…
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…
Facilitating Advanced Sentinel-2 Analysis Through a Simplified Computation of Nadir BRDF Adjusted Reflectance
David Montero, Miguel D. Mahecha, César Aybar +2
The Sentinel-2 (S2) mission from the European Space Agency's Copernicus program provides essential data for Earth surface analysis. Its Level-2A products deliver high-to-medium res…
On-Demand Earth System Data Cubes
David Montero, César Aybar, Chaonan Ji +4
Advancements in Earth system science have seen a surge in diverse datasets. Earth System Data Cubes (ESDCs) have been introduced to efficiently handle this influx of high-dimension…
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…