Proposals

• B1 Proposal
• B2 Proposal

Interview Slides

Reporting

• Continuous Reporting: 01/09/2015 - 28/02/2017
• Mid-term Report: 01/09/2015 - 28/02/2018

Outreach Presentations

• Advanced Applications in AI (AAA)
• Algorithms and Analysis (AAA)
• Applied Analytics for Agriculture (AAA)
• Atmospheric and Aerial Analysis (AAA)

The SEDAL project is an interdisciplinary effort to develop novel statistical learning methods to analyze Earth Observation (EO) satellite data. The project focuses on improving prediction models, discovering knowledge and causal relations in EO data, and contributing to various remote sensing applications.

Through the development of kernel learning frameworks and graphical models, SEDAL aims to address current limitations in EO data analysis. The project’s methodologies involve enhancing statistical regression models, learning graphical models and causal inference, and conducting case studies from local to global scales.

SEDAL’s outreach efforts include multiple presentations and reports aimed at disseminating research findings and engaging with the broader scientific community. These presentations cover a wide range of applications and advancements in artificial intelligence and EO data analysis.