Mapping Indicators for Morphological Informality in Nairobi, Kenya Using Satellite Imagery

Over one billion people globally live in slums, informal settlements, and other deprived areas. However, maps of deprived areas are often unavailable or over-simplistic, distinguishing only between slums and formal areas. Recent research advocates for a multidimensional approach to better account for the complexity of deprivation. Previous studies have mapped the unplanned urbanization domain of deprivation using morphometrics derived from building footprint data. This study explores leveraging Earth observation data for scalability and regular updates by using high-resolution satellite imagery and deep learning to map indicators for morphological informality in Nairobi, Kenya. The proposed model combines a ResNet backbone with three classification heads to map the two indicators: irregular settlement layout (ISL) and small, dense structures (SDS), alongside building presence. The model was trained on automatically generated reference data using building footprint morphometrics and clustering, and its outputs were validated through community-sourced annotations obtained via participatory action research. The results demonstrate the potential of high-resolution satellite imagery for mapping ISL (F1 80.90) and SDS (F1 78.73). Nonetheless, further research is required on the geographic transferability of the proposed method.