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Research Spending & Results

Award Detail

Doing Business As Name:TERRA COVER, INC.
  • Ankush Khandelwal
  • (612) 300-7969
Award Date:06/11/2021
Estimated Total Award Amount: $ 255,880
Funds Obligated to Date: $ 255,880
  • FY 2021=$255,880
Start Date:06/15/2021
End Date:03/31/2022
Transaction Type:Grant
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.041
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:SBIR Phase I: A Physics Guided Machine Learning Framework for Monitoring Rivers using Satellite Imagery
Federal Award ID Number:2045444
DUNS ID:117375091
Program:SBIR Phase I
Program Officer:
  • Peter Atherton
  • (703) 292-8772

Awardee Location

Street:4771 W Alameda AVE
Awardee Cong. District:01

Primary Place of Performance

Organization Name:TERRA COVER, INC.
Street:2700 S HOLLY ST APT 201
Cong. District:01

Abstract at Time of Award

The broader impact of this Small Business Innovation Research (SBIR) Phase I project will be to provide near-real time information of changes in the spatial extent (flood mapping) and flow of rivers (water resource management) to insurance, energy, and agricultural stakeholders. Effective management of water resources and associated risks has become a major challenge for society. Floods are common disasters around the world and droughts lead to major disruptions to economies and a loss of life. This project will leverage artificial intelligence, and peta-bytes of satellite imagery to implement a physics guided data-intensive approach for advancing global hydrological modeling. The project will provide efficient and accurate imagery-derived observations of water dynamics in rivers at relatively low computational cost (compared with ground sensors) in a user-friendly web environment. This will be a significant step towards improving the modelling and forecasting of water resources around the world. This Small Business Innovation Research (SBIR) Phase I project aims to develop advanced artificial intelligence techniques to track surface water changes in rivers across the globe using vast amounts of satellite imagery. While conventional artificial intelligence techniques are purely driven by data, the proposed technology incorporates known physical laws into these algorithms. This physics guided approach makes these techniques much more robust to atmospheric disturbances (clouds, shadows, haze, etc.), and enables synergistic use of imagery datasets at different resolutions which are two major issues with satellite imagery analysis. Furthermore, the proposed uncertainty quantification techniques will enable domain experts to incorporate their local knowledge about river flows into the framework to refine results. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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