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Gridded spatial datasets arise naturally in environmental, climatic, meteorological, and ecological settings. Each grid point encapsulates a vector of variables representing different measures of interest. Gridded datasets tend to be voluminous since they encapsulate observations for long timescales. Visualizing such datasets poses significant challenges stemming from the need to preserve interactivity, manage I/O overheads, and cope with data volumes. Here we present our methodology to significantly alleviate I/O requirements by leveraging deep neural network-based models and a distributed, in-memory cache to facilitate interactive visualizations. Our benchmarks demonstrate that deploying our lightweight models coupled with back-end caching and prefetching schemes can reduce the client's query response time by 92.3% while maintaining a high perceptual quality with a PSNR (peak signal-to-noise ratio) of 38.7 dB. 

Gridded spatial datasets arise naturally in environmental, climatic, meteorological, and ecological settings. Each grid point encapsulates a vector of variables representing different measures of interest. Gridded datasets tend to be voluminous since they encapsulate observations for long timescales. Visualizing such datasets poses significant challenges stemming from the need to preserve interactivity, manage I/O overheads, and cope with data volumes. Here we present our methodology to significantly alleviate I/O requirements by leveraging deep neural network-based models. 

Multi-spectral satellite images that remotely sense the Earth's surface at regular intervals are often contaminated due to occlusion by clouds. Remote sensing imagery captured via satellites, drones, and aircraft has successfully influenced a wide range of fields such as monitoring vegetation health, tracking droughts, and weather forecasting, among others. Researchers studying the Earth's surface are often hindered while gathering reliable observations due to contaminated reflectance values that are sensitive to thin, thick, and cirrus clouds, as well as their shadows. In this study, we propose a deep learning network architecture, CloudNet, to alleviate cloud-occluded remote sensing imagery captured by Landsat-8 satellite for both visible and non-visible spectral bands. We propose a deep neural network model trained on a distributed storage cluster that leverages historical trends within Landsat-8 imagery while complementing this analysis with high-resolution Sentinel-2 imagery. Our empirical benchmarks profile the efficiency of the CloudNet model with a range of cloud-occluded pixels in the input image. We further compare our CloudNet's performance with state-of-the-art deep learning approaches such as SpAGAN and Resnet. We propose a novel method, dynamic hierarchical transfer learning, to reduce computational resource requirements while training the model to achieve the desired accuracy. Our model regenerates features of cloudy images with a high PSNR accuracy of 34.28 dB.