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Found 8 papers, 3 papers with code
Spectral Synthesis for Satellite-to-Satellite Translation
These satellites have different vantage points above the earth and different spectral imaging bands resulting in inconsistent imagery from one to another.
High-Dimensional Similarity Search with Quantum-Assisted Variational Autoencoder
Quantum machine learning is touted as a potential approach to demonstrate quantum advantage within both the gate-model and the adiabatic schemes.
A framework for deep learning emulation of numerical models with a case study in satellite remote sensing
A difficult test for deep learning-based emulation, which refers to function approximation of numerical models, is to understand whether they can be comparable to traditional forms of surrogate models in terms of computational efficiency while simultaneously reproducing model results in a credible manner.
Temporal Interpolation of Geostationary Satellite Imagery with Task Specific Optical Flow
Applications of satellite data in areas such as weather tracking and modeling, ecosystem monitoring, wildfire detection, and land-cover change are heavily dependent on the trade-offs to spatial, spectral and temporal resolutions of observations.
Quantum-assisted associative adversarial network: Applying quantum annealing in deep learning
We present an algorithm for learning a latent variable generative model via generative adversarial learning where the canonical uniform noise input is replaced by samples from a graphical model.
Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning
Furthermore, we find that the lognormal distribution, which can handle skewed distributions, produces quality uncertainty estimates at the extremes.
DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution
The impacts of climate change are felt by most critical systems, such as infrastructure, ecological systems, and power-plants.
Intercomparison of Machine Learning Methods for Statistical Downscaling: The Case of Daily and Extreme Precipitation
Statistical downscaling of global climate models (GCMs) allows researchers to study local climate change effects decades into the future.
