78 citations · 88 across the 6 of their papers we have counts for
6 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…
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…
Assessment of Sentinel-2 spatial and temporal coverage based on the scene classification layer
Cristhian Sanchez, Francisco Mena, Marcela Charfuelan +2
Since the launch of the Sentinel-2 (S2) satellites, many ML models have used the data for diverse applications. The scene classification layer (SCL) inside the S2 product provides…
Impact Assessment of Missing Data in Model Predictions for Earth Observation Applications
Francisco Mena, Diego Arenas, Marcela Charfuelan +2
Earth observation (EO) applications involving complex and heterogeneous data sources are commonly approached with machine learning models. However, there is a common assumption tha…
Adaptive Fusion of Multi-view Remote Sensing data for Optimal Sub-field Crop Yield Prediction
Francisco Mena, Deepak Pathak, Hiba Najjar +11
Accurate crop yield prediction is of utmost importance for informed decision-making in agriculture, aiding farmers, and industry stakeholders. However, this task is complex and dep…