A team of students will combine cutting-edge, high-resolution satellite imagery with a state-of-the-art AI and pattern-recognition framework to improve restoration outcomes across sub-Saharan Africa. Students will map trees both inside forests and across farms and villages using a deep learning model, then link those maps to socioeconomic factors and biophysical variables such as soils, terrain, rainfall, and fire history to identify the best places and times to plant. The team will deliver an interactive decision tool that turns these insights into site-specific recommendations for partners on the ground. This work creates a practical, scalable foundation for climate-smart restoration by boosting survival, reducing costs, and guiding matching between tree species with sites.
Project Leads: Tong Qiu and Hanshi Chen
Project Managers: Zhuohong Li
