78 citations · 133 across the 12 of their papers we have counts for
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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…
Missing Data as Augmentation in the Earth Observation Domain: A Multi-View Learning Approach
Francisco Mena, Diego Arenas, Andreas Dengel
Multi-view learning (MVL) leverages multiple sources or views of data to enhance machine learning model performance and robustness. This approach has been successfully used in the…
Increasing the Robustness of Model Predictions to Missing Sensors in Earth Observation
Francisco Mena, Diego Arenas, Andreas Dengel
Multi-sensor ML models for EO aim to enhance prediction accuracy by integrating data from various sources. However, the presence of missing data poses a significant challenge, part…
In the Search for Optimal Multi-view Learning Models for Crop Classification with Global Remote Sensing Data
Francisco Mena, Diego Arenas, Andreas Dengel
Studying and analyzing cropland is a difficult task due to its dynamic and heterogeneous growth behavior. Usually, diverse data sources can be collected for its estimation. Althoug…