78 citations · 197 across the 22 of their papers we have counts for
23 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…
The Directed Prediction Change - Efficient and Trustworthy Fidelity Assessment for Local Feature Attribution Methods
Kevin Iselborn, David Dembinsky, Adriano Lucieri +1
The utility of an explanation method critically depends on its fidelity to the underlying machine learning model. Especially in high-stakes medical settings, clinicians and regulat…
Multi-modal Co-learning for Earth Observation: Enhancing single-modality models via modality collaboration
Francisco Mena, Dino Ienco, Cassio F. Dantas +2
Multi-modal co-learning is emerging as an effective paradigm in machine learning, enabling models to collaboratively learn from different modalities to enhance single-modality pred…
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…
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…
Can Multitask Learning Enhance Model Explainability?
Hiba Najjar, Bushra Alshbib, Andreas Dengel
Remote sensing provides satellite data in diverse types and formats. The usage of multimodal learning networks exploits this diversity to improve model performance, except that the…
Intrinsic Explainability of Multimodal Learning for Crop Yield Prediction
Hiba Najjar, Deepak Pathak, Marlon Nuske +1
Multimodal learning enables various machine learning tasks to benefit from diverse data sources, effectively mimicking the interplay of different factors in real-world applications…
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…
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…
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…
Data-Centric Machine Learning for Earth Observation: Necessary and Sufficient Features
Hiba Najjar, Marlon Nuske, Andreas Dengel
The availability of temporal geospatial data in multiple modalities has been extensively leveraged to enhance the performance of machine learning models. While efforts on the desig…
Quantum Annealing-Based Algorithm for Efficient Coalition Formation Among LEO Satellites
Supreeth Mysore Venkatesh, Antonio Macaluso, Marlon Nuske +2
The increasing number of Low Earth Orbit (LEO) satellites, driven by lower manufacturing and launch costs, is proving invaluable for Earth observation missions and low-latency inte…
XAI-Guided Enhancement of Vegetation Indices for Crop Mapping
Hiba Najjar, Francisco Mena, Marlon Nuske +1
Vegetation indices allow to efficiently monitor vegetation growth and agricultural activities. Previous generations of satellites were capturing a limited number of spectral bands,…
Explainability of Sub-Field Level Crop Yield Prediction using Remote Sensing
Hiba Najjar, Miro Miranda, Marlon Nuske +2
Crop yield forecasting plays a significant role in addressing growing concerns about food security and guiding decision-making for policymakers and farmers. When deep learning is e…
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…
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…
Qubit-efficient Variational Quantum Algorithms for Image Segmentation
Supreeth Mysore Venkatesh, Antonio Macaluso, Marlon Nuske +2
Quantum computing is expected to transform a range of computational tasks beyond the reach of classical algorithms. In this work, we examine the application of variational quantum…
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…
Q-Seg: Quantum Annealing-Based Unsupervised Image Segmentation
Supreeth Mysore Venkatesh, Antonio Macaluso, Marlon Nuske +2
We present Q-Seg, a novel unsupervised image segmentation method based on quantum annealing, tailored for existing quantum hardware. We formulate the pixel-wise segmentation proble…
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…