70 citations · 246 across the 12 of their papers we have counts for
12 papers
Anomaly Detection in Satellite Videos using Diffusion Models
Akash Awasthi, Son Ly, Jaer Nizam +7
The definition of anomaly detection is the identification of an unexpected event. Real-time detection of extreme events such as wildfires, cyclones, or floods using satellite data…
Spectral Synthesis for Satellite-to-Satellite Translation
Thomas Vandal, Daniel McDuff, Weile Wang +2
Earth observing satellites carrying multi-spectral sensors are widely used to monitor the physical and biological states of the atmosphere, land, and oceans. These satellites have…
High-Dimensional Similarity Search with Quantum-Assisted Variational Autoencoder
Nicholas Gao, Max Wilson, Thomas Vandal +3
Recent progress in quantum algorithms and hardware indicates the potential importance of quantum computing in the near future. However, finding suitable application areas remains a…
DeepSat V2: Feature Augmented Convolutional Neural Nets for Satellite Image Classification
Qun Liu, Saikat Basu, Sangram Ganguly +4
Satellite image classification is a challenging problem that lies at the crossroads of remote sensing, computer vision, and machine learning. Due to the high variability inherent i…
A framework for deep learning emulation of numerical models with a case study in satellite remote sensing
Kate Duffy, Thomas Vandal, Weile Wang +2
Numerical models based on physics represent the state-of-the-art in earth system modeling and comprise our best tools for generating insights and predictions. Despite rapid growth…
Temporal Interpolation of Geostationary Satellite Imagery with Task Specific Optical Flow
Thomas Vandal, Ramakrishna Nemani
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-of…
Progressively Growing Generative Adversarial Networks for High Resolution Semantic Segmentation of Satellite Images
Edward Collier, Kate Duffy, Sangram Ganguly +8
Machine learning has proven to be useful in classification and segmentation of images. In this paper, we evaluate a training methodology for pixel-wise segmentation on high resolut…
Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning
Thomas Vandal, Evan Kodra, Jennifer Dy +3
Deep Learning (DL) methods have been transforming computer vision with innovative adaptations to other domains including climate change. For DL to pervade Science and Engineering (…
DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution
Thomas Vandal, Evan Kodra, Sangram Ganguly +3
The impacts of climate change are felt by most critical systems, such as infrastructure, ecological systems, and power-plants. However, contemporary Earth System Models (ESM) are r…
A Theoretical Analysis of Deep Neural Networks for Texture Classification
Saikat Basu, Manohar Karki, Robert DiBiano +4
We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative represe…
DeepSat - A Learning framework for Satellite Imagery
Saikat Basu, Sangram Ganguly, Supratik Mukhopadhyay +3
Satellite image classification is a challenging problem that lies at the crossroads of remote sensing, computer vision, and machine learning. Due to the high variability inherent i…
Learning Sparse Feature Representations using Probabilistic Quadtrees and Deep Belief Nets
Saikat Basu, Manohar Karki, Sangram Ganguly +3
Learning sparse feature representations is a useful instrument for solving an unsupervised learning problem. In this paper, we present three labeled handwritten digit datasets, col…