This workshop is aimed at demonstrating the utility of geospatial machine learning in addressing large-scale food security issues in Africa. Participants will learn how to construct an end-to-end ML pipeline for mapping crops in a region of interest using satellite imagery. The workshop will cover key concepts of geospatial machine learning such as data acquisition, spatial analysis, data engineering, machine learning, and inference. Focusing on agriculture to mitigate the effects of climate change in Africa. 

By the end of the workshop, participants will have a foundational understanding of geospatial machine learning applications in agriculture. They will be equipped with practical skills to utilize satellite imagery for automated spatial mapping and have insights into forming collaborative networks to address climate-related challenges in Africa. 

- Basic to intermediate knowledge in Python and machine learning. 
- Familiarity with satellite imagery and geospatial data is beneficial but not mandatory. 
- Installation of necessary software and tools as detailed in the workshop's GitHub repository README file. 
- Participants are encouraged to install and set up the required tools prior to the workshop for a more efficient hands-on session.