SBIR Phase I: High Resolution, Synthetic Satellite Imagery of the Earth

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be to address a strong commercial and scientific need for historical and most current satellite data for visualization and analytical purposes with regional-to-global coverage, frequent revisit, and high spatial detail at an affordable price. It will allow companies to improve their value proposition, competitive edge, and to differentiate their services. The technology will be valuable in operational settings at the large providers of commercial satellite imagery, to individual users, and in a host of commercial applications such as insurance, agriculture, and emergency. The development of the proposed technology will contribute to the advancement of scientific knowledge especially in the geospatial arena and to market spillovers. By dramatically simplifying access to accurate historical and most current imagery for any time and place, this project will provide companies, researchers, educators, students, and regular citizens with a valuable tool for visualizing and exploring our changing planet and will contribute to increasing public engagement with science and technology. Further, the analytical capabilities offered by the imagery have great potential in scientific applications thus contributing to partnerships between academia and industry and improving datasets for research and education. This Small Business Innovation Research (SBIR) Phase I project will demonstrate the technical feasibility of operationally synthesizing accurate global, high spatial / high temporal satellite imagery of the Earth. The complexity of accessing, processing, and analyzing various sources of satellite imagery creates a significant barrier to its use. Synthesis of regionally and globally continuous high spatial and high temporal resolution imagery is a challenge as in addition to inherent differences in spatial and temporal resolutions of the source data, the new models need to account for enormous data volumes and sparse coverage of high spatial resolution imagery. Existing techniques to handle these challenges have severe limitations which curtail their use outside of the research arena. The proposed technology will overcome these limitations by implementing advanced data fusion algorithms to combine various sources of satellite data to synthesize imagery for any given date and location while preserving the best spatial and temporal attributes of the data sources. The algorithms will be robust, easily automated, scalable, deliver accurate data, are usable in operational settings, and will provide spatially consistent and temporally relevant imagery which will empower businesses with regional and global outreach to make better decisions with better data.