78 citations · 144 across the 11 of their papers we have counts for
11 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…
Quantum-Assisted Correlation Clustering
Antonio Macaluso, Supreeth Mysore Venkatesh, Diego Arenas +2
This work introduces a hybrid quantum-classical method to correlation clustering, a graph-based unsupervised learning task that seeks to partition the nodes in a graph based on pai…
i-QLS: Quantum-supported Algorithm for Least Squares Optimization in Non-Linear Regression
Supreeth Mysore Venkatesh, Antonio Macaluso, Diego Arenas +2
We propose an iterative quantum-assisted least squares (i-QLS) optimization method that leverages quantum annealing to overcome the scalability and precision limitations of prior q…
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…
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…
Predicting Crop Yield With Machine Learning: An Extensive Analysis Of Input Modalities And Models On a Field and sub-field Level
Deepak Pathak, Miro Miranda, Francisco Mena +11
We introduce a simple yet effective early fusion method for crop yield prediction that handles multiple input modalities with different temporal and spatial resolutions. We use hig…
A Comparative Assessment of Multi-view fusion learning for Crop Classification
Francisco Mena, Diego Arenas, Marlon Nuske +1
With a rapidly increasing amount and diversity of remote sensing (RS) data sources, there is a strong need for multi-view learning modeling. This is a complex task when considering…
Common Practices and Taxonomy in Deep Multi-view Fusion for Remote Sensing Applications
Francisco Mena, Diego Arenas, Marlon Nuske +1
The advances in remote sensing technologies have boosted applications for Earth observation. These technologies provide multiple observations or views with different levels of info…