This project uses machine learning and satellite imagery to map and analyze urban sprawl in Nairobi, Kenya.
# Exploring Urban Sprawl in Nairobi, Kenya
This project uses machine learning and satellite imagery to map and analyze urban sprawl in Nairobi, Kenya. As one of East Africa's fastest-growing cities, Nairobi faces challenges from rapid and unregulated expansion, especially in informal settlements like Kibera and Korogocho.
In this project, we apply a supervised classification model (Random Forest) to Landsat imagery and land use maps to:
- Detect and quantify urban growth
- Differentiate between formal and informal settlements
- Track spatial development trends over time
Our goal is to support data-driven planning efforts focused on public health, infrastructure, and environmental management.
**Data sources**: Landsat-5, Landsat-7, Landsat-8, and Nairobi Land Use Map
**Classes**: Informal settlements, formal settlements, vegetation