Collaborative Research: CAIG: Weakly Supervised Learning to Address Label-Data Irregularit — NSF Award to University of Colorado a
Arctic sea ice plays a crucial role in regulating global climate. Accordingly, monitoring sea ice conditions and mapping its properties, such as type, extent, and concentration, are important for climate monitoring as well as marine navigation and near- or off-shore operations. This project will address data availabili
| Award title | Collaborative Research: CAIG: Weakly Supervised Learning to Address Label-Data Irregularit |
|---|---|
| Award ID | 2531101 |
| Awardee | University of Colorado at Denver |
| City | AURORA |
| State | CO |
| Amount obligated | $805,132 |
| Principal investigator | Farnoush Banaei-Kashani |
| Program | GEO CI - GEO Cyberinfrastrctre |
| Start date | 09/01/2025 |
| Abstract | Arctic sea ice plays a crucial role in regulating global climate. Accordingly, monitoring sea ice conditions and mapping its properties, such as type, extent, and concentration, are important for climate monitoring as well as marine navigation and near- or off-shore operations. This project will address data availability and bottlenecks in manually producing maps of sea ice by applying artificial intelligence (AI) methods to the problem. The project will introduce novel AI driven methods to effe |
| Source | NSF Awards |
$799/mo
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