SAS - Climate Change Theme
The Challenge
- Lack of reliable and accurate data integration models
- Climate change data consisting of GHG emissions are currently dependent on local data sources, captured by MoFAIC from different sectors nationwide. Additionally, remote sensing alone is not capable of providing accurate information, which results in discrepancies when compared to in-situ data.
- This challenge addresses the lack of accurate models that can provide highly reliable and accurate GHG emission concentration data.
Winning TeamFarmin
Farmin (Farmin Website)
Proposed Solution
Enhancing the Monitoring of Greenhouse Gases Emissions Using Satellites Data and Artificial Intelligence
Expected Deliverables
- Utilize artificial intelligence and global, updated, and widely available remote sensing data.
- Monitor air pollution estimates over time, at random locations with high accuracy, down to 100 m.
- Identify meteorological variables (winds, dust, humidity).
- Showcase vegetation levels.
- Showcase elevation data.
- Monitor urban parameters (population and roads).