6 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…
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…
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…