The University of Namibia is pioneering a project that leverages artificial intelligence to enhance climate change research.
During a recent public lecture, Professor Emmanuel Kreike from Princeton University introduced the 3TMAAP project, which aims to utilize historical aerial photographs dating back to the 1940s. This initiative addresses the limitations of current satellite imagery, which only provides data from around the year 2000.
UNAM’s Innovative Approach to Climate Research
The 3TMAAP project is a collaborative effort between UNAM and Princeton University. It focuses on using machine learning to analyze historical aerial photographs, offering a deeper understanding of environmental changes over time. Professor Kreike emphasized that existing satellite data does not capture the full scope of changes, as it only reflects the situation from the 2000s onward.
By examining aerial photographs taken almost every decade since the 1940s, researchers can trace the evolution of Namibia’s landscapes. This methodology allows for a comprehensive view of how environmental factors have shifted over the decades.
Namibia: Key figures on Historical Aerial Photography
- Historical aerial photographs dating back to the 1940s
- Current satellite imagery provides information from around the year 2000
Strengthening Climate Models with Historical Data
The project aims to create three-dimensional models from the historical photographs, enabling researchers, students, and communities to visualize changes in landscapes, villages, and ecosystems. This extended historical data will enhance climate models, providing insights into past environmental changes and improving predictions for future climate trends.
Next Steps for the 3TMAAP Project
- Development of machine-learning methodologies for analyzing aerial photographs
- Creation of three-dimensional models from historical data
- Engagement with communities and students for educational purposes
This innovative project positions Namibia at the forefront of climate change research by utilizing historical data to inform future predictions.
