Measuring Agricultural Adaptation to Climate Change in Zambia Using Satellite Imagery and Machine Learning
In this project, we conduct a case study of Zambia to demonstrate a recently developed machine learning pipeline. We use the “Multi-task Observations using Satellite Imagery & Kitchen Sinks” (MOSAIKS) machine learning approach. In this approach, we use processed numerical data from satellite images and agricultural survey data to develop machine learning models that predict various agricultural variables over time.
Group Members: Andrew Bartnik, Carl (Carlo) Broderick, Gabrielle Smith, Hailey Veirs
Clients: UCSB Bren School
