4 citations · 9 across the 11 of their papers we have counts for
7 papers · 1 filter
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…
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…
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,…
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…
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…