5 papers
YieldSAT: A Multimodal Benchmark Dataset for High-Resolution Crop Yield Prediction
Miro Miranda, Deepak Pathak, Patrick Helber +10
Crop yield prediction requires substantial data to train scalable models. However, creating yield prediction datasets is constrained by high acquisition costs, heterogeneous data q…
Informed Learning for Estimating Drought Stress at Fine-Scale Resolution Enables Accurate Yield Prediction
Miro Miranda, Marcela Charfuelan, Matias Valdenegro Toro +1
Water is essential for agricultural productivity. Assessing water shortages and reduced yield potential is a critical factor in decision-making for ensuring agricultural productivi…
An Analysis of Temporal Dropout in Earth Observation Time Series for Regression Tasks
Miro Miranda, Francisco Mena, Andreas Dengel
Missing instances in time series data impose a significant challenge to deep learning models, particularly in regression tasks. In the Earth Observation field, satellite failure or…
On What Depends the Robustness of Multi-source Models to Missing Data in Earth Observation?
Francisco Mena, Diego Arenas, Miro Miranda +1
In recent years, the development of robust multi-source models has emerged in the Earth Observation (EO) field. These are models that leverage data from diverse sources to improve…
Exploring Physics-Informed Neural Networks for Crop Yield Loss Forecasting
Miro Miranda, Marcela Charfuelan, Andreas Dengel
In response to climate change, assessing crop productivity under extreme weather conditions is essential to enhance food security. Crop simulation models, which align with physical…