Understanding the physical and chemical processes of Earth's atmosphere, land, and ocean presents significant scientific challenges. Due in part to the societal impact of addressing these challenges, recent years have seen significant investments in a large number of satellite and ground-based sensors dedicated to Earth observation. The observations from these sensors are used to estimate important geophysical properties such as temperature, clouds, aerosols, greenhouse gases, snow and ice, and used in scientific studies aimed at climate modeling, weather forecasting, air quality monitoring, and disease management. Current techniques from spatial statistics and data fusion fall short of what is needed because of computational constraints, difficulties in modeling and parameter estimation, or inability to provide uncertainty estimates. The objective of this project is to develop methods that help best utilize large quantities of multi-source observations from satellite and ground-based instruments for Earth observation having different capabilities regarding coverage, resolution, and quality.

This project develops a novel discriminative modeling framework for fusion of multi-sensor remote sensing data based on the Gaussian conditional random field model. The framework is designed to be flexible, robust, and computationally efficient, and hence suitable for use on large spatio-temporal data sets. It allows learning from a mixture of labeled and unlabeled data with partially observable attributes, in the presence of sampling bias. The methods will be implemented in an open source, user-friendly, software tool for use by practitioners. The resulting tools will be evaluated on the problems of atmospheric aerosol and surface level pollution estimation, which are some of the important problems in climate research and environmental science.

The broader impacts of this project include methodological advances in the current state of the art in spatio-temporal data mining and geostatistics as well as Earth remote sensing. It will enable improved characterization of the effects of aerosols on the Earth's radiation budget and climate. The data fusion framework is directly applicable to estimations of many other atmospheric, land, and ocean properties. The research activities in this project are integrated with education. They will help broaden the participation of students ranging from doctoral to K-12 level and increasing diversity in Computer Science through already established channels at Temple University for engaging students from underrepresented groups.

Additional details about the project can be found at: www.dabi.temple.edu/~vucetic/nsf_fusion.htm

National Science Foundation (NSF)
Division of Information and Intelligent Systems (IIS)
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Sylvia J. Spengler
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Temple University
United States
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