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Computational Science of Data-Intensive Remote Sensing

NOAA's mission to advance understanding and predict changes in the Earth's environment, as well as manage coastal and marine resources, requires it to develop and maintain expertise in the computational science of data-intensive remote sensing. NOAA's ability to measure, monitor, store, and distribute information about the Earth's environment is growing at an unprecedented pace that will only accelerate in the future. This growth creates tremendous challenges and opportunities for NOAA.

New tools and methods are constantly being developed to understand an increasingly data-intensive environment, because current methods may be running into their limits as we approach progressively larger volumes of information. Since many of the current techniques are often not well suited for processing some of the newer data populations, this environment requires fundamental advances in applying mathematics and computational science to remote sensing, far beyond data management and system integration. New techniques and fast computational methods must be developed to give NOAA scientists the ability to automatically analyze, compress, visualize, and search the rising sea of data. With such developments, NOAA will enhance its ability to provide accurate and new data products in a timely fashion.

The Computational Science of Data-Intensive Remote Sensing Group at CREST is developing methods and technology to give NOAA scientists and their collaborators better tools for information exploration, visualization, data integration and compression. 

Interests

  • Information Exploration
  • Collaborative Computing
  • Multi-spectral Compression
  • Virtual Sensing
  • Data Integration

Goals

  • Provide NOAA with expertise in the computational science of data-intensive remote sensing needed to support NOAA's mission to advance understanding and predict changes in the Earth's environment, as well as manage coastal and marine resources.
  • Develop tools and methods to help NOAA meet its need to analyze, monitor, visualize, store, and distribute information about the Earth's environment.

People

  • Irina Gladkova, Associate Professor, Computer Science Department
  • Michael Grossberg, Assistant Professor, Computer Science Department `
  • Paul Alabi, Satellite Receiving Station Manager, NOAA CREST
  • Hannah Aizenman, PhD Student
  • Fazlul Shahriar, PhD Student
  • George Bonev, Master's Student

Data and Products

  1. Yellowstone Modis Database
  2. MODIS Band 6 restoration code



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