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Challenges in interactive visualizations over satellite data collections stem primarily from their inherent data volumes. Enabling interactive visualizations of such data results in both processing and I/O (network and disk) on the server side. These are further exacerbated by multiple, concurrent requests issued by different clients. Hotspots may also arise when multiple users are interested in a particular geographical extent. We propose a novel methodology to support interactive visualizations over voluminous satellite imagery. Our system, codenamed Glance, generates models that once installed on the client side, substantially alleviate resource requirements on the server side. Our system dynamically generates imagery during zoom-in operations. Glance also supports image refinements using partial high-resolution information when available. Glance is based broadly on a deep Generative Adversarial Network, and our model is space-efficient to facilitate memory-residency at the clients. We supplement Glance with a module to estimate rendering errors when using the model to generate imagery as opposed to a resource-intensive query-and-retrieve operation to the server. Benchmarks to profile our methodology show substantive improvements in interactivity with up to 23x reduction in time lags without utilizing GPU and 297x-6627x reduction while harnessing GPU. Further, the perceptual quality of the images from our generative model is robust with PSNR values ranging from 32.2-40.5, depending on the scenario and upscale factor. 

We propose EVOKE, a model based on progressive Generative Adversarial Networks, that dynamically reconstructs high-resolution imagery during zoom-in operations using in-memory historical low-resolution images and is space-efficient to facilitate memory-residency at the clients. 

Geospatial data collections are now available in a multiplicity of domains. The accompanying data volumes, variety, and diversity of encoding formats within these collections have all continued to grow. These data offer opportunities to extract patterns, understand phenomena, and inform decision making by fitting models to the data. To ensure accuracy and effectiveness, these models need to be constructed at geospatial extents/scopes that are aligned with the nature of decision-making — administrative boundaries such as census tracts, towns, counties, states etc. This entails construction of a large number of models and orchestrating their accompanying resource requirements (CPU, RAM and I/O) within shared computing clusters. In this study, we describe our methodology to facilitate model construction at scale by substantively alleviating resource requirements while preserving accuracy. Our benchmarks demonstrate the suitability of our methodology.