  Urbanization is a common phenomenon in developing countries and it poses
serious challenges when not managed effectively. Lack of proper planning and
management may cause the encroachment of urban fabrics into reserved or special
regions which in turn can lead to an unsustainable increase in population.
Ineffective management and planning generally leads to depreciated standard of
living, where physical hazards like traffic accidents and disease vector
breeding become prevalent. In order to support urban planners and policy makers
in effective planning and accurate decision making, we investigate urban
land-use in sub-Saharan Africa. Land-use dynamics serves as a crucial parameter
in current strategies and policies for natural resource management and
monitoring. Focusing on Nairobi, we use an efficient deep learning approach
with patch-based prediction to classify regions based on land-use from 2004 to
2018 on a quarterly basis. We estimate changes in land-use within this period,
and using the Autoregressive Integrated Moving Average (ARIMA) model, our
results forecast land-use for a given future date. Furthermore, we provide
labelled land-use maps which will be helpful to urban planners.
