A Cloud-Native Workflow for Publishing, Discovering, Processing, and Visualizing Geospatial Data
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Engineering topics
Publications and source records attributed to Brian Freitag.
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Linear regression and histogram matching based techniques have been widely used to minimize the surface reflectance difference between two similar satellite observations such as Landsat-8/9 and Sentinel-2A/B products [1]. However, regionally or globally derived conversion factors may not be suitable for all land cover types and locations, resulting in noticeable residual differences between the sensors. Generative Adversarial Network (GAN) has shown promise in the field of image processing for domain or style transfer[2]. In this work we aim to minimize the surface reflectance difference between Landsat and Sentinel-2 products based on GAN.
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NASA's Visualization, Exploration, and Data Analysis (VEDA) project is an open-source science cyberinfrastructure for data processing, visualization, exploration, and geographic information systems (GIS) capabilities. Developed collaboratively and mostly reusing existing open-source components, VEDA consolidates GIS delivery mechanisms, processing platforms, analysis services, and visualization tools and provides an ecosystem of open tools for addressing Earth science research and application needs through the public-facing VEDA Dashboard. In this presentation, Dr. Freitag will provide an overview of VEDA and how it can potentially serve the AOS community.
Why? - Interdisciplinary science depends on large amount of Earth science data and computational resources - Working with these datasets is non-trivial - Big data science requires advanced distributed computing knowledge What? VEDA is an open platform that brings key Earth science datasets next to open source tools for data processing, analysis, visualization, and exploration in a managed and more accessible computing environment.
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This study investigates the relationship between the Great Salt Lake (GSL) decline and air quality, focusing on PM2.5 concentrations and their impacts on public health. As the GSL continues to shrink, it has become a significant source of dust emissions, posing serious health and environmental risks to the surrounding areas. There search examines how diminishing water levels in the GSL, driven by climate change, agricultural water use, and urban development, affect local populations' mental health. Adopting an interdisciplinary approach, the study considers the long-term effects of air quality on mental health in relation to inorganic particulate matter, gaseous pollutants, and social vulnerability.
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